Bernoulli Factory Algorithms

Peter Occil

Abstract: This page catalogs algorithms to turn coins biased one way into coins biased another way, also known as Bernoulli factories. It provides step-by-step instructions to help programmers implement these Bernoulli factory algorithms. This page also contains algorithms to exactly sample probabilities that are irrational numbers, using only random bits, which is related to the Bernoulli factory problem. This page is focused on methods that exactly sample a given probability without introducing new errors, assuming "truly random" numbers are available. The page links to a Python module that implements several Bernoulli factories.

2020 Mathematics Subject Classification: 68W20, 60-08, 60-04.

Introduction

We're given a coin that shows heads with an unknown probability, λ. The goal is to use that coin (and possibly also a fair coin) to build a "new" coin that shows heads with a probability that depends on λ, call it f(λ). This is the Bernoulli factory problem.

And this page catalogs algorithms to solve this problem for a wide variety of functions, algorithms known as Bernoulli factories.

Many of these algorithms were suggested in (Flajolet et al., 2010)[^1], but without step-by-step instructions in many cases. This page provides these instructions to help programmers implement the Bernoulli factories they describe. The Python module bernoulli.py includes implementations of several Bernoulli factories.

This page also contains algorithms to exactly sample probabilities that are irrational numbers, which is related to the Bernoulli factory problem. (An irrational number is a number that can't be written as a ratio of two integers.) Again, many of these algorithms were suggested in (Flajolet et al., 2010)[^1].

This page is focused on methods that exactly sample the probability described, without introducing rounding errors or other errors beyond those already present in the inputs (and assuming that we have a source of independent and unbiased random bits).

For extra notes, see: Supplemental Notes for Bernoulli Factory Algorithms

About This Document

This is an open-source document; for an updated version, see the source code or its rendering on GitHub. You can send comments on this document on the GitHub issues page. See "Requests and Open Questions" for a list of things about this document that I seek answers to.

I encourage readers to implement any of the algorithms given in this page, and report their implementation experiences. This may help improve this page.

Contents

About Bernoulli Factories

A Bernoulli factory (Keane and O'Brien 1994)[^2] is an algorithm that takes an input coin (a method that returns 1, or heads, with an unknown probability, or 0, or tails, otherwise) and returns 0 or 1 with a probability that depends on the input coin's probability of heads.

Example: A Bernoulli factory algorithm can take a coin that returns heads with probability λ and produce a coin that returns heads with probability exp(−λ). In this example, exp(−λ) is the factory function.

Keane and O'Brien (1994)[^2] showed that a function f that maps [0, 1] (or a subset of it) to [0, 1] admits a Bernoulli factory if and only if—

The following shows some functions that are factory functions and some that are not. In the table below, ϵ is a number greater than 0 and less than 1/2.

Function f(λ) Domain Can f be a factory function?
0 [0, 1] Yes; constant.
1 [0, 1] Yes; constant.
1/2 (0, 1) Yes; constant.
1/4 if λ<1/2, and 3/4 elsewhere (0, 1) No; discontinuous.
2*λ [0, 1] or [0, 1/2) No; not polynomially bounded since its graph touches 1 somewhere in the interval (0, 1) on its domain.[^3].
1−2*λ [0, 1] or [0, 1/2) No; not polynomially bounded since its graph touches 0 somewhere in the interval (0, 1) on its domain.
2*λ [0, 1/2−ϵ] Yes; continuous and polynomially bounded on domain (Keane and O'Brien 1994)[^2].
min(2 * λ, 1 − ϵ) [0, 1] Yes; continuous and polynomially bounded on domain (Huber 2014, introduction)[^4].
0 if λ = 0, or exp(−1/λ) otherwise (0, 1) No; not polynomially bounded since it moves away from 0 more slowly than any polynomial.
ϵ if λ = 0, or exp(−1/λ) + ϵ otherwise (0, 1) Yes; continuous and bounded away from 0 and 1.

If f's domain includes 0 and/or 1 (so that the input coin is allowed to return 0 every time or 1 every time, respectively), then f can be a factory function only if—

  1. f is constant on its domain, or is continuous and polynomially bounded on its domain, and
  2. f(0) equals 0 or 1 whenever 0 is in the domain of f, and
  3. f(1) equals 0 or 1 whenever 1 is in the domain of f,

unless outside randomness (besides the input coin) is available.

Algorithms

This section will show algorithms for a number of factory functions, allowing different kinds of probabilities to be sampled from input coins.

The algorithms as described here do not always lead to the best performance. An implementation may change these algorithms as long as they produce the same results as the algorithms as described here.

Notes:

  1. Most of the algorithms assume that a source of independent and unbiased random bits is available, in addition to the input coins. But in many cases, they can be implemented using nothing but those coins as a source of randomness. See the appendix for details.
  2. Bernoulli factory algorithms that sample the probability f(λ) act as unbiased estimators of f(λ) (their "long run average" equals f(λ)). See the appendix for details.

Implementation Notes

This section shows implementation notes that apply to the algorithms in this article. They should be followed to avoid introducing error in the algorithms.

In the following algorithms:

Algorithms for General Functions of λ

This section describes general-purpose algorithms for sampling probabilities that are polynomials, rational functions, or functions in general.

Certain Polynomials

Any polynomial can be written in Bernstein form as—

$$\sum_{i=0}^n {n \choose i} \lambda^i (1-\lambda)^{n-i} a[i],$$

where n is the polynomial's degree and a[i] are its n plus one coefficients.

But the only polynomials that admit a Bernoulli factory are those whose coefficients are all in the interval [0, 1] (once the polynomials are written in Bernstein form), and these polynomials are the only functions that can be simulated with a fixed number of coin flips (Goyal and Sigman 2012[^6]; Qian et al. 2011)[^7]; see also Wästlund 1999, section 4[^8]). Goyal and Sigman give an algorithm for simulating these polynomials, which is given below.

  1. Flip the input coin n times, and let j be the number of times the coin returned 1 this way.[^9]
  2. Return a number that is 1 with probability a[j], or 0 otherwise.

For certain polynomials with duplicate coefficients, the following is an optimized version of this algorithm, not given by Goyal and Sigman:

  1. Set j to 0 and i to 0. If n is 0, return 0.
  2. If i is n or greater, or if the coefficients a[k], with k in the interval [j, j+(ni)], are all equal, return a number that is 1 with probability a[j], or 0 otherwise.
  3. Flip the input coin. If it returns 1, add 1 to j.
  4. Add 1 to i and go to step 2.

And here is another optimized algorithm:

  1. Set j to 0 and i to 0. If n is 0, return 0. Otherwise, generate a uniform(0, 1) random variate, call it u.
  2. If u is less than a lower bound of the lowest coefficient, return 1. Otherwise, if u is less than (or equal to) an upper bound of the highest coefficient, go to the next step. Otherwise, return 0.
  3. If i is n or greater, or if the coefficients a[k], with k in the interval [j, j+(ni)], are all equal, return a number that is 1 if u is less than a[j], or 0 otherwise.
  4. Flip the input coin. If it returns 1, add 1 to j.
  5. Add 1 to i and go to step 3.

Because the coefficients a[i] must be in the interval [0, 1], some or all of them can themselves be coins with unknown probability of heads. In that case, the first algorithm can read as follows:

  1. Flip the input coin n times, and let j be the number of times the coin returned 1 this way.
  2. If a[j] is a coin, flip it and return the result. Otherwise, return a number that is 1 with probability a[j], or 0 otherwise.

Notes:

  1. Each a[i] acts as a control point for a 1-dimensional Bézier curve, where λ is the relative position on that curve, the curve begins at a[0], and the curve ends at a[n]. For example, given control points 0.2, 0.3, and 0.6, the curve is at 0.2 when λ = 0, and 0.6 when λ = 1. (The curve, however, is not at 0.3 when λ = 1/2; in general, Bézier curves do not cross their control points other than the first and the last.)
  2. The problem of simulating polynomials in Bernstein form is related to stochastic logic, which involves simulating probabilities that arise out of Boolean functions (functions that use only AND, OR, NOT, and exclusive-OR operations) that take a fixed number of bits as input, where each bit has a separate probability of being 1 rather than 0, and output a single bit (for further discussion see (Qian et al. 2011)[^7], Qian and Riedel 2008[^10]).
  3. These algorithms can serve as an approximate way to simulate any function f that maps the interval [0, 1] to [0, 1], whether continuous or not. In this case, a[j] is calculated as f(j/n), so that the resulting polynomial closely approximates the function. In fact, if f is continuous, it's possible to choose n high enough to achieve a given maximum error (this is a result of the so-called "Weierstrass approximation theorem"). For more information, see my Supplemental Notes on Bernoulli Factories.

Examples:

  1. Take the following parabolic function discussed in Thomas and Blanchet (2012)[^11]: (1−4*(λ−1/2)2)*c, where c is in the interval (0, 1). This is a polynomial of degree 2 that can be rewritten as −4*c*λ2+4*c*λ, so that this power form has coefficients (0, 4*c, −4*c) and a degree (n) of 2. By rewriting the polynomial in Bernstein form (such as via the matrix method by Ray and Nataraj (2012)[^12]), we get coefficients (0, 2*c, 0). Thus, for this polynomial, a[0] is 0, a[1] is 2*c, and a[2] is 0. Thus, if c is in the interval (0, 1/2], we can simulate this function as follows: "Flip the input coin twice. If exactly one of the flips returns 1, return a number that is 1 with probability 2*c and 0 otherwise. Otherwise, return 0." For other values of c, the algorithm requires rewriting the polynomial in Bernstein form, then elevating the degree of the rewritten polynomial enough times to bring its coefficients in [0, 1]; the required degree approaches infinity as c approaches 1.[^13]
  2. The conditional construction, mentioned in Flajolet et al. (2010)[^1], has the form—
    (λ) * a[0] + (1 − λ) * a[1].
    This is a degree-1 polynomial in Bernstein form with variable λ and coefficients a[0] and a[1]. The following algorithm simulates this polynomial: "Flip the λ input coin. If the result is 0, flip the a[0] input coin and return the result. Otherwise, flip the a[1] input coin and return the result." Special cases of the conditional construction include complement, mean, product, and logical OR; see "Other Factory Functions".

 

Multiple coins. Niazadeh et al. (2021)[^14] describes monomials (involving one or more coins) of the form λ[1]a[1] * (1−λ[1])b[1]*λ[2]a[2] * (1−λ[2])b[2]* ... * λ[n]a[n] * (1−λ[n])b[n], where there are n coins, λ[i] is the probability of heads of coin i, and a[i] ≥ 0 and b[i] ≥ 0 are parameters for coin i (specifically, of a+b flips, the first a flips must return heads and the rest must return tails to succeed).

  1. For each i in [1, n]:
    1. Flip the λ[i] input coin a[i] times. If any of the flips returns 0, return 0.
    2. Flip the λ[i] input coin b[i] times. If any of the flips returns 1, return 0.
  2. Return 1.

The same paper also describes polynomials that are weighted sums of this kind of monomials, namely polynomials of the form P = ∑j = 1, ..., k c[j]*M[j](λ), where there are k monomials, M[j](.) identifies monomial j, λ identifies the coins' probabilities of heads, and c[j] ≥ 0 is the weight for monomial j.

Let C be the sum of all c[j]. To simulate the probability P/C, choose one of the monomials with probability proportional to its weight (see "Weighted Choice With Replacement"), then run the algorithm above on that monomial (see also "Convex Combinations", later).

The following is a special case:

Certain Rational Functions

A rational function is a ratio of polynomials.

According to Mossel and Peres (2005)[^15], a function that maps the open interval (0, 1) to (0, 1) can be simulated by a finite-state machine if and only if the function can be written as a rational function whose coefficients are rational numbers.

The following algorithm is suggested from the Mossel and Peres paper and from (Thomas and Blanchet 2012)[^11]. It assumes the rational function is written as D(λ)/E(λ), where—

Here, d[i] is akin to the number of "passing" n-bit words with i ones, and e[i] is akin to that number plus the number of "failing" n-bit words with i ones. (Because of the assumptions, D and E are polynomials that map the closed interval [0, 1] to [0, 1].)

The algorithm follows.

  1. Flip the input coin n times, and let heads be the number of times the coin returned 1 this way.
  2. Choose 0, 1, or 2 with probability proportional to these weights: [e[heads] − d[heads], d[heads], choose(n, heads) − e[heads]]. If 0 or 1 is chosen this way, return it. Otherwise, go to step 1.

Notes:

  1. In the formulas above—

    • d[i] can be replaced with δ[i] * choose(n,i), where δ[i] is a rational number in the interval [0, 1] (and thus expresses the probability that a given word is a "passing" word among all n-bit words with i ones), and
    • e[i] can be replaced with η[i] * choose(n,i), where η[i] is a rational number in the interval [0, 1] (and thus expresses the probability that a given word is a "passing" or "failing" word among all n-bit words with i ones),

    and then δ[i] and η[i] can be seen as control points for two different 1-dimensional Bézier curves, where the δ curve is always on or "below" the η curve. For each curve, λ is the relative position on that curve, the curve begins at δ[0] or η[0], and the curve ends at δ[n] or η[n]. See also the next section.

  2. This algorithm could be modified to avoid additional randomness besides the input coin flips by packing the coin flips into an n-bit word and looking up whether that word is "passing", "failing", or neither, among all n-bit words with j ones, but this can be impractical (in general, a lookup table of size 2n first has to be built in a setup step; as n grows, the table size grows exponentially). Moreover, this approach works only if d[i] and e[i] are integers (or if d[i] is replaced with floor(d[i]) and e[i] with ceil(e[i]) (Nacu and Peres 2005)[^16], but this, of course, suffers from rounding error when done in this algorithm). See also (Thomas and Blanchet 2012)[^11].

  3. As with polynomials, this algorithm (or the one given later) can serve as an approximate way to simulate any factory function, via a rational function that closely approximates that function. The higher n is, the better this approximation, and in general, a degree-n rational function approximates a given function better than a degree-n polynomial. However, to achieve a given error tolerance with a rational function, the degree n as well as d[i] and e[i] have to be optimized. This is unlike the polynomial case where only the degree n has to be optimized.

Example: Take the function f(λ) = 1/(λ−2)2. This is a rational function, in this case a ratio of two polynomials that are both non-negative on the interval [0, 1]. One algorithm to simulate f is:
(1) Flip the input coin twice, and let heads be the number of times the coin returned 1 this way.
(2) Depending on heads, choose 0, 1, or 2 with probability proportional to the following weights: heads=0 → [3, 1, 0], heads=1 → [1, 1, 2], heads=2 → [0, 1, 3]; if 0 or 1 is chosen this way, return it; otherwise, go to step 1.
Here is how f was prepared to derive this algorithm:
(1) Take the numerator 1, and the denominator (λ−2)2. Rewrite the denominator as 1*λ2 − 4*λ + 4.
(2) Rewrite the numerator and denominator into homogeneous polynomials (polynomials whose terms have the same degree) of degree 2; see the "homogenizing" section in "Preparing Rational Functions". The result is (1, 2, 1) and (4, 4, 1) respectively.
(3) Divide both polynomials (actually their coefficients) by the same value so that both polynomials are 1 or less. An easy (but not always best) choice is to divide them by their maximum coefficient, which is 4 in this case. The result is d = (1/4, 1/2, 1/4), e = (1, 1, 1/4).
(4) Prepare the weights as given in step 2 of the original algorithm. The result is [3/4, 1/4, 0], [1/2, 1/2, 1], and [0, 1/4, 3/4], for different counts of heads. Because the weights in this case are multiples of 1/4, they can be simplified to integers without affecting the algorithm: [3, 1, 0], [1, 1, 2], [0, 1, 3], respectively.

"Dice Enterprise" special case. The following algorithm implements a special case of the "Dice Enterprise" method of Morina et al. (2022)[^17]. The algorithm returns one of m outcomes (namely X, an integer in [0, m)) with probability PX(λ) / (P0(λ) + P1(λ) + ... + Pm−1(λ)), where λ is the input coin's probability of heads and m is 2 or greater. Specifically, the probability is a rational function, or ratio of polynomials. Here, all the Pk(λ) are in the form of polynomials as follows:

Any rational function that admits a Bernoulli factory can be brought into the form just described, as detailed in the appendix under "Preparing Rational Functions". In this algorithm, let R[j] be the sum of jth coefficients of the polynomials (with j starting at 0). First, define the following operation:

Then the algorithm is as follows:

  1. Create two empty lists: blist and ulist.
  2. Set state1 to the position of the first non-zero item in R. Set state2 to the position of the last non-zero item in R. In both cases, positions start at 0. If all the items in R are zeros, return 0.
  3. Flip the input coin and append the result (which is 0 or 1) to the end of blist. Generate a uniform(0, 1) random variate and append it to the end of ulist.
  4. (Monotonic coupling from the past (Morina et al., 2022)[^17], (Propp and Wilson 1996)[^18].) Set i to the number of items in blist minus 1, then while i is 0 or greater:
    1. Let b be the item at position i (starting at 0) in blist, and let u be the item at that position in ulist.
    2. Get the new state given state1, b, u, and n, and set state1 to the new state.
    3. Get the new state given state2, b, u, and n, and set state2 to the new state.
    4. Subtract 1 from i.
  5. If state1 and state2 are not equal, go to step 2.
  6. Let b(j) be coefficient a[state1] of the polynomial for j. Choose an integer in [0, m) with probability proportional to these weights: [b(0), b(1), ..., b(m−1)]. Then return the chosen integer.

Notes:

  1. If there are only two outcomes, then this is the special Bernoulli factory case; the algorithm would then return 1 with probability P1(λ) / (P0(λ) + P1(λ)).
  2. If R[j] = choose(n, j), steps 1 through 5 have the same effect as counting the number of ones from n input coin flips (which would be stored in state1 in this case), but unfortunately, these steps wouldn't be more efficient. In this case, PA is equivalent to "1 if state is greater than floor(n/2), and state/(n+1−state) otherwise", and PB is equivalent to "1 if state is less than floor(n/2), and (nstate)/(state+1) otherwise".

Example: Let P0(λ) = 2*λ*(1−λ) and P1(λ) = (4*λ*(1−λ))2/2. The goal is to produce 1 with probability P1(λ) / (P0(λ) + P1(λ)). After preparing this function (and noting that the maximum degree is n = 4), we get the coefficient sums R = (0, 2, 12, 2, 0). Since R begins and ends with 0, step 2 of the algorithm sets state1 and state2, respectively, to the position of the first or last nonzero item, namely 1 or 3. (Alternatively, because R begins and ends with 0, we could include a third polynomial, namely the constant P2(λ) = 0.001, so that the new coefficient sums would be R′ = (0.001, 10.004, 12.006, 2.006, 0.001) [formed by adding the coefficient 0.001*choose(n, i) to the sum at i, starting at i = 0]. Now we would run the algorithm using R′, and if it returns 2 [meaning that the constant polynomial was chosen], we would try again until the algorithm no longer returns 2.)

Certain Power Series

A power series is a function written as— $$f(\lambda) = \sum_{i\ge 0} a_i (g(\lambda))^i,$$ where $a_i$ are coefficients and $g(\lambda)$ is a function in the variable $\lambda$. Not all power series sum to a definite value, but all power series that matter in this article do, and they must be factory functions.


For certain power series with non-negative coefficients, Mendo (2019)[^19] gave the following algorithm. The algorithm uses parameter v and is given below. A table of supported power series will follow the algorithm.

  1. Set dsum to 0 and i to 1.
  2. Do the following process repeatedly, until this algorithm returns a value:
    1. Flip the input coin. If it returns v, return 1.
    2. If the power series has infinitely many coefficients, or if i is equal to or less than the number of coefficients:
      • With probability c[i]/(CSdsum), return 0. Otherwise, add c[i] to dsum. (CS is defined later.)
    3. Add 1 to i.

A table of supported power series follows:

No. Power Series Algorithm
1 f(λ) = 1 − (c[1] * (1 − λ) + ... + c[i] * (1 − λ)i + ...) With probability CS, run the algorithm above with v=1 and return the result. Otherwise, return 0.
2 f(λ) = (c[1] * (1 − λ) + ... + c[i] * (1 − λ)i + ...) With probability CS, run the algorithm above with v=1 and return 1 minus the result. Otherwise, return 1.
3 f(λ) = (c[1] * λ + ... + c[i] * λi + ...) With probability CS, run the algorithm above with v=0 and return 1 minus the result. Otherwise, return 0.
4 f(λ) = 1 − (c[1] * λ + ... + c[i] * λi + ...) With probability CS, run the algorithm above with v=0 and return the result. Otherwise, return 1.

In the table above, c[i] ≥ 0 are the coefficients of the series. CS is the sum of all the coefficients, must be 1 or less, and should be a rational number. This implies that—

Note: For series 1 with CS=1 (the main form in the Mendo paper), a rewrite of this algorithm shows this is actually a special case of convex combinations — simply generate X with probability c[X], then flip a coin that shows heads with probability (1 − λ)X*c[X]/c[X] = (1 − λ)X (or flip the input coin X times and show heads only if all the flips return 0), then return either 0 if heads is shown, or 1 otherwise.


For certain other power series with non-negative coefficients (especially those that equal 1 or greater somewhere in the interval [0, 1]), Nacu and Peres (2005, proposition 16)[^16] gave the following algorithm. It works on functions of the form—

f(λ) = d[0] + d[1] * λ + d[2] * λ2 + ...,

where each d[i] is 0 or greater, and takes the following parameters:

B is not a parameter, but is the maximum allowed value for λ (probability of heads) and in the interval (0, 1).

  1. Create a ν input coin that does the following: "(1) Set n to 0. (2) With probability ϵ/t, go to the next substep. Otherwise, add 1 to n and repeat this substep. (3) With probability 1 − d[n]*tn, return 0. (4) Run a linear Bernoulli factory n times, using the (λ) input coin, x/y = 1/(tϵ), and ϵ = ϵ. If any of these runs returns 0, return 0. Otherwise, return 1."
  2. Run a linear Bernoulli factory once, using the ν input coin described earlier, x/y = t/ϵ, and ϵ = ϵ, and return the result.

(Łatuszyński et al. 2009/2011)[^20] gave an algorithm that works for a wide class of series and other constructs that converge to the desired probability from above and from below.

For example, suppose the following:

Example: Let $f(\lambda) = (1/2)\lambda^0 - (1/4)\lambda^2 + (1/8)\lambda^4 - ...$. Then $(a_i) = (1/2, 0, -1/4, 0, 1/8, ...)$ (for example, $a_0 = 1/2$) and deleting the zeros leads to $(d_i) = (1/2, -1/4, 1/8, ...)$ (for example, $d_0 = 1/2$), which meets the requirements above.

Then the algorithm below simulates $f(\lambda)$ given a coin that shows heads (returns 1) with probability $g(\lambda)$.

General martingale algorithm:

  1. Set u to abs($d_0$) ($d_0$ is the first nonzero coefficient of the series), set w to 1, set to 0, and set n to 1.
  2. Generate a uniform(0, 1) random variate ret.
  3. Do the following process repeatedly, until this algorithm returns a value:
    1. If w is not 0, run a Bernoulli factory algorithm for $g(\lambda)$ (if $g(\lambda) = \lambda$, this is done by flipping the input coin), then multiply w by the result of the run.
    2. If $a_n$ is greater than 0: Set u to + w * $a_n$, then, if no further nonzero coefficients follow $a_n$, set to u.
    3. If $a_n$ is less than 0: Set to uw * abs($a_n$), then, if no further nonzero coefficients follow $a_n$, set u to .
    4. If ret is less than (or equal to) , return 1. Otherwise, if ret is less than u, add 1 to n. Otherwise, return 0. (If ret is a uniform PSRN, these comparisons should be done via the URandLessThanReal algorithm, which is described in my article on PSRNs.)

Note: This algorithm supports more functions than in section 3.1 of Łatuszyński et al. (2019/2011), which supports only power series whose coefficients alternate in sign and decrease in absolute value, with no zeros in between nonzero coefficients. However, the algorithm above uses that paper's framework. A proof of its correctness is given in the appendix.


Note: Not all power series that admit a Bernoulli factory are covered by the algorithms in this section. They include:

General Factory Functions

A coin with unknown probability of heads of λ can be turned into a coin with probability of heads of f(λ), where f is any factory function, via an algorithm that builds randomized bounds on f(λ) based on the outcomes of the coin flips. These randomized bounds come from two sequences of polynomials:

The following algorithm can be used to simulate factory functions via polynomials. In the algorithm:

The algorithm implements the reverse-time martingale framework (Algorithm 4) in Łatuszyński et al. (2009/2011)[^20] and the degree-doubling suggestion in Algorithm I of Flegal and Herbei (2012)[^21], although an error in Algorithm I is noted below. The first algorithm follows.

  1. Generate a uniform(0, 1) random variate, call it ret.
  2. Set and ℓt to 0. Set u and ut to 1. Set lastdegree to 0, and set ones to 0.
  3. Set degree so that the first pair of polynomials has degree equal to degree and has coefficients all lying in [0, 1]. For example, this can be done as follows: Let fbound(n) be the minimum value for fbelow(n, k) and the maximum value for fabove(n,k) with k in the interval [0, n]; then set degree to 1; then while fbound(degree) returns an upper or lower bound that is less than 0 or greater than 1, multiply degree by 2; then go to the next step.
  4. Set startdegree to degree.
  5. (The remaining steps are now done repeatedly until the algorithm finishes by returning a value.) Flip the input coin t times, where t is degreelastdegree. For each time the coin returns 1 this way, add 1 to ones.
  6. Calculate and u as follows:
    1. Define FB(a, b) as follows: Let c be choose(a, b). (Optionally, multiply c by 2a; see note 3.) Calculate fbelow(a, b) as lower and upper bounds LB and UB that are accurate enough that floor(LB*c) = floor(UB*c), then return floor(LB*c)/c.
    2. Define FA(a, b) as follows: Let c be choose(a, b). (Optionally, multiply c by 2a; see note 3.) Calculate fabove(a, b) as lower and upper bounds LB and UB that are accurate enough that ceil(LB*c) = ceil(UB*c), then return ceil(LB*c)/c.
    3. Set to FB(degree, ones) and set u to FA(degree, ones).
  7. (This step and the next find the means of the previous and of u given the current coin flips.) If degree equals startdegree, set ℓs to 0 and us to 1. (Algorithm I of Flegal and Herbei 2012 doesn't take this into account.)
  8. If degree is greater than startdegree:
    1. Let nh be choose(degree, ones), and let k be min(lastdegree, ones).
    2. Set ℓs to ∑j=0,...,k FB(lastdegree,j)*choose(degreelastdegree, onesj)*choose(lastdegree,j)/nh.
    3. Set us to ∑j=0,...,k FA(lastdegree,j)*choose(degreelastdegree, onesj)*choose(lastdegree,j)/nh.
  9. Let m be (utℓt)/(usℓs). Set ℓt to ℓt+(ℓs)*m, and set ut to ut−(usu)*m.
  10. If ret is less than (or equal to) ℓt, return 1. If ret is less than ut, go to the next step. If neither is the case, return 0. (If ret is a uniform PSRN, these comparisons should be done via the URandLessThanReal algorithm, which is described in my article on PSRNs.)
  11. (Find the next pair of polynomials and restart the loop.) Set lastdegree to degree, then increase degree so that the next pair of polynomials has degree equal to a higher value of degree and gets closer to the target function (for example, multiply degree by 2). Then, go to step 5.

Another algorithm, given in Thomas and Blanchet (2012)[^11], was based on the one from Nacu and Peres (2005)[^16]. That algorithm is not given here, however.

Notes:

  1. The efficiency of this algorithm depends on many things, including how "smooth" f is (Holtz et al. 2011)[^22] and how easy it is to calculate the appropriate values for fbelow and fabove. The best way to implement fbelow and fabove for a given function f will require a deep mathematical analysis of that function. For more information, see my Supplemental Notes on Bernoulli Factories.
  2. In some cases, a single pair of polynomial sequences may not converge quickly to the desired function f, especially when f is not "smooth" enough. An intriguing suggestion from Thomas and Blanchet (2012)[^11] is to use multiple pairs of polynomial sequences that converge to f, where each pair is optimized for particular ranges of λ: first flip the input coin several times to get a rough estimate of λ, then choose the pair that's optimized for the estimated λ, and run either algorithm in this section on that pair.
  3. Normally, the algorithm works only if λ is in the interval (0, 1), If λ can be 0 or 1 (meaning the input coin is allowed to return 1 every time or 0 every time), then based on a suggestion in Holtz et al. (2011)[^22], the c in FA and FB can be multiplied by 2a (as shown in step 6) to ensure correctness for every value of λ.

Algorithms for General Irrational Constants

This section shows general-purpose algorithms to generate heads with a probability equal to an irrational number (a number that isn't a ratio of two integers), when that number is known by its digit or series expansion, continued fraction, or continued logarithm.

But on the other hand, probabilities that are rational constants are trivial to simulate. If fair coins are available, the ZeroOrOne method, which is described in my article on random sampling methods, should be used. If coins with unknown probability of heads are available, then a randomness extraction method should be used to turn them into fair coins.

Digit Expansions

Probabilities can be expressed as a digit expansion (of the form 0.dddddd...). The following algorithm returns 1 with probability p and 0 otherwise, where p is a probability in the half-open interval [0, 1). (The number 0 is also an infinite digit expansion of zeros, and the number 1 is also an infinite digit expansion of base-minus-ones.) Irrational numbers always have infinite digit expansions, which must be calculated "on-the-fly".

In the algorithm (see also (Brassard et al., 2019)[^22], (Devroye 1986, p. 769)[^23]), BASE is the digit base, such as 2 for binary or 10 for decimal.

  1. Set u to 0 and k to 1.
  2. Set u to (u * BASE) + v, where v is a uniform random integer in the interval [0, BASE) (if BASE is 2, then v is simply an unbiased random bit). Calculate pa, which is an approximation to p such that abs(ppa) ≤ BASEk. Set pk to pa's digit expansion up to the k digits after the point. Example: If p is π/4, BASE is 10, and k is 5, then pk = 78539.
  3. If pk + 1 <= u, return 0.[^24] If pk - 2 >= u, return 1. If neither is the case, add 1 to k and go to step 2.

Continued Fractions

The following algorithm simulates a probability expressed as a simple continued fraction of the following form: 0 + 1 / (a[1] + 1 / (a[2] + 1 / (a[3] + ... ))). The a[i] are the partial denominators, none of which may have an absolute value less than 1. Inspired by (Flajolet et al., 2010, "Finite graphs (Markov chains) and rational functions")[^1], I developed the following algorithm.

Algorithm 1. This algorithm works only if each a[i]'s absolute value is 1 or greater and a[1] is greater than 0, but otherwise, each a[i] may be negative and/or a non-integer. The algorithm begins with pos equal to 1. Then the following steps are taken.

  1. Set k to a[pos].
  2. If the partial denominator at pos is the last, return a number that is 1 with probability 1/k and 0 otherwise.
  3. If a[pos] is less than 0, set kp to k − 1 and s to 0. Otherwise, set kp to k and s to 1. (This step accounts for negative partial denominators.)
  4. Do the following process repeatedly until this run of the algorithm returns a value:
    1. With probability kp/(1+kp), return a number that is 1 with probability 1/kp and 0 otherwise.
    2. Do a separate run of the currently running algorithm, but with pos = pos + 1. If the separate run returns s, return 0.

Algorithm 2.

A generalized continued fraction has the form 0 + b[1] / (a[1] + b[2] / (a[2] + b[3] / (a[3] + ... ))). The a[i] are the same as before, but the b[i] are the partial numerators. The following are two algorithms to simulate a probability in the form of a generalized continued fraction.

The following algorithm works only if each ratio b[i]/a[i] has an absolute value of 1 or less, but otherwise, each b[i] and each a[i] may be negative and/or a non-integer. This algorithm employs an equivalence transform from generalized to simple continued fractions. The algorithm begins with pos and r both equal to 1. Then the following steps are taken.

  1. Set r to 1 / (r * b[pos]), then set k to a[pos] * r. (k is the partial denominator for the equivalent simple continued fraction.)
  2. If the partial numerator/denominator pair at pos is the last, return a number that is 1 with probability 1/abs(k) and 0 otherwise.
  3. Set kp to abs(k) and s to 1.
  4. Set r2 to 1 / (r * b[pos + 1]). If a[pos + 1] * r2 is less than 0, set kp to kp − 1 and s to 0. (This step accounts for negative partial numerators and denominators.)
  5. Do the following process repeatedly until this run of the algorithm returns a value:
    1. With probability kp/(1+kp), return a number that is 1 with probability 1/kp and 0 otherwise.
    2. Do a separate run of the currently running algorithm, but with pos = pos + 1 and r = r. If the separate run returns s, return 0.

Algorithm 3. This algorithm works only if each ratio b[i]/a[i] is 1 or less and if each b[i] and each a[i] is greater than 0, but otherwise, each b[i] and each a[i] may be a non-integer. The algorithm begins with pos equal to 1. Then the following steps are taken.

  1. If the partial numerator/denominator pair at pos is the last, return a number that is 1 with probability b[pos]/a[pos] and 0 otherwise.
  2. Do the following process repeatedly until this run of the algorithm returns a value:
    1. With probability a[pos]/(1 + a[pos]), return a number that is 1 with probability b[pos]/a[pos] and 0 otherwise.
    2. Do a separate run of the currently running algorithm, but with pos = pos + 1. If the separate run returns 1, return 0.

See the appendix for a correctness proof of Algorithm 3.

Notes:

Continued Logarithms

The continued logarithm (Gosper 1978)[^25], (Borwein et al., 2016)[^26] of a number in the open interval (0, 1) has the following continued fraction form: 0 + (1 / 2c[1]) / (1 + (1 / 2c[2]) / (1 + ...)), where c[i] are the coefficients of the continued logarithm and all 0 or greater. I have come up with the following algorithm that simulates a probability expressed as a continued logarithm expansion.

The algorithm begins with pos equal to 1. Then the following steps are taken.

  1. If the coefficient at pos is the last, return a number that is 1 with probability 1/(2c[pos]) and 0 otherwise.
  2. Do the following process repeatedly until this run of the algorithm returns a value:
    1. Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), return a number that is 1 with probability 1/(2c[pos]) and 0 otherwise.
    2. Do a separate run of the currently running algorithm, but with pos = pos + 1. If the separate run returns 1, return 0.

For a correctness proof, see the appendix.

Certain Algebraic Numbers

A method to sample a probability equal to a polynomial's root appears in a French-language article by Penaud and Roques (2002)[^27]. The following is an implementation of that method, using the discussion in the paper's section 1 and Algorithm 2, and incorporates a correction to Algorithm 2. The algorithm takes a polynomial as follows:

And the algorithm returns 1 with probability equal to the root, and 0 otherwise. The root R is known as an algebraic number because it satisfies the polynomial equation P(R) = 0. The algorithm follows.

  1. Set r to 0 and d to 2.
  2. Do the following process repeatedly, until this algorithm returns a value:
    1. Generate an unbiased random bit, call it z.
    2. Set t to (r*2+1)/d.
    3. If P(0) > 0:
      1. If z is 1 and P(t) is less than 0, return 0.
      2. If z is 0 and P(t) is greater than 0, return 1.
    4. If P(0) < 0:
      1. If z is 1 and P(t) is greater than 0, return 0.
      2. If z is 0 and P(t) is less than 0, return 1.
    5. Set r to r*2+z, then multiply d by 2.

Example (Penaud and Roques 2002)[^27]: Let P(x) = 1 − xx2. This is a polynomial whose only root on [0, 1] is 2/(1+sqrt(5)), that is, 1 divided by the golden ratio or 1/φ or about 0.618, and P(0) > 0. Then given P, the algorithm above samples the probability 1/φ exactly.

Certain Converging Series

A general-purpose algorithm was given by Mendo (2020/2021)[^28] that can simulate any probability in the open interval (0, 1), as long as it can be rewritten as a series—

The algorithm follows.

  1. Set ϵ to 1, then set n, lamunq, lam, s, and k to 0 each.
  2. Add 1 to k, then add s/(2k) to lam.
  3. If lamunq+ϵlam + 1/(2k), go to step 8.
  4. If lamunq > lam + 1/(2k), go to step 8.
  5. If lamunq > lam + 1/(2k+1) and lamunq+ϵ < 3/(2k+1), go to step 8.
  6. Add a[n] to lamunq and set ϵ to err[n].
  7. Add 1 to n, then go to step 3.
  8. Let bound be lam+1/(2k). If lamunq+ϵbound, set s to 0. Otherwise, if lamunq > bound, set s to 2. Otherwise, set s to 1.
  9. Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), go to step 2. Otherwise, return a number that is 0 if s is 0, 1 if s is 2, or an unbiased random bit (either 0 or 1 with equal probability) otherwise.

If a, given above, is instead a sequence that converges to the base-2 logarithm of a probability in the open interval (0, 1), the following algorithm I developed simulates that probability. For simplicity's sake, even though logarithms for such probabilities are negative, all the a[i] must be 0 or greater (and thus are the negated values of the already negative logarithm approximations) and must form a nondecreasing sequence, and all the err[i] must be 0 or greater.

  1. Set intinf to floor(max(0, abs(a[0]))). (This is the absolute integer part of the first term in the series, or 0, whichever is greater.)
  2. If intinf is greater than 0, generate unbiased random bits until a zero bit or intinf bits were generated this way. If a zero was generated this way, return 0.
  3. Generate an exponential random variate E with rate ln(2). This can be done, for example, by using the algorithm given in "More Algorithms for Arbitrary-Precision Sampling". (We take advantage of the exponential distribution's memoryless property: given that an exponential random variate E is greater than intinf, E minus intinf has the same distribution.)
  4. Set n to 0.
  5. Do the following process repeatedly until the algorithm returns a value:
    1. Set inf to max(0, a[n]), then set sup to min(0, inf+err[n]).
    2. If E is less than inf+intinf, return 0. If E is less than sup+intinf, go to the next step. If neither is the case, return 1.
    3. Set n to 1.

The case when the sequence a converges to a natural logarithm rather than a base-2 logarithm is trivial by comparison. Again for this algorithm, all the a[i] must be 0 or greater and form a nondecreasing sequence, and all the err[i] must be 0 or greater.

  1. Generate an exponential random variate E (with rate 1).
  2. Set n to 0.
  3. Do the following process repeatedly until the algorithm returns a value:
    1. Set inf to max(0, a[n]), then set sup to min(0, inf+err[n]).
    2. If E is less than inf+intinf, return 0. If E is less than sup+intinf, go to the next step. If neither is the case, return 1.
    3. Set n to 1.

Note: Mendo (2020/2021)[^28] as well as Carvalho and Moreira (2022)[^29] discuss how to find error bounds on "cutting off" a series that work for many infinite series. This can be helpful in finding the appropriate sequences a and err needed for the first algorithm in this section.

Examples:

Other General Algorithms

 

Convex Combinations

Assume we have one or more input coins hi(λ) that return heads with a probability that depends on λ. (The number of coins may be infinite.) The following algorithm chooses one of these coins at random then flips that coin. Specifically, the algorithm generates 1 with probability equal to the following weighted sum: g(0) * h0(λ) + g(1) * h1(λ) + ..., where g(i) is the probability that coin i will be chosen, hi is the function simulated by coin i, and all the g(i) sum to 1. See (Wästlund 1999, Theorem 2.7)[^8]. (Alternatively, the algorithm can be seen as returning heads with probability E[hX(λ)], that is, the expected value, or "long-run average", of hX where X is the number that identifies the randomly chosen coin.)

  1. Generate a random integer X in some way. For example, it could be a uniform random integer in [1, 6], or it could be a Poisson random variate. (Specifically, the number X is generated with probability g(X). If every g(i) is a rational number, the following algorithm can generate X: "(1) Set X to 0 and d to 1. (2) With probability g(X)/d, return X; otherwise subtract g(X) from d, add 1 to X, and repeat this step.")
  2. Flip the coin represented by X and return the result.

Notes:

  1. Building convex combinations. Assume we have a function of the form f(λ) = ∑n=0,1,... wn(λ), where wn are continuous functions whose maximum values in the domain [0, 1] sum to 1 or less. Let g(n) be the probability that a randomly chosen number X is n, such that g(0) + g(1) + ... = 1. Then by generating X and flipping a coin with probability of heads of wX(λ)/g(X), we can simulate the probability f(λ) as the convex combination— $$f(\lambda)=\sum_{n\ge 0} g(n) \frac{w_n(\lambda)}{g(n)},$$ but this works only if the following conditions are met for each integer n≥0:

    • $1 \ge g(n) \ge w_n(\lambda) \ge 0$ for every $\lambda$ in the interval [0, 1] (which roughly means that $w_n$ is no greater than $g(n)$).
    • The function $w_n(\lambda)/g(n)$ admits a Bernoulli factory (which it won't if it touches 0 or 1 inside the interval (0, 1), but isn't constant, for example).

    See also Mendo (2019)[^19].

  2. Constants with non-negative series expansions. A special case of note 1. Let g be as in note 1. Assume we have a constant with the following series expansion: $$c=a_0+a_1+a_2+...,$$ where—

    • $a_n$ are each 0 or greater and sum to 1 or less, and
    • $1 \ge g(n) \ge a_n \ge 0$ for each integer $n\ge 0$.

    Then by generating X and flipping a coin with probability of heads of aX/g(X), we can simulate the probability c as the convex combination— $$c=\sum_{n\ge 0} g(n) \frac{a_n}{g(n)}.$$

Examples:

  1. Generate X, a Poisson random variate with mean μ, then flip the input coin. With probability 1/(1+X), return the result of the coin flip; otherwise, return 0. This corresponds to g(i) being the Poisson probabilities and the coin for hi returning 1 with probability 1/(1+i), and 0 otherwise. The probability that this method returns 1 is E[1/(1+X)], or (exp(μ)−1)/(exp(μ)*μ).
  2. (Wästlund 1999)[^8]: Generate a Poisson random variate X with mean 1, then flip the input coin X times. Return 0 if any of the flips returns 1, or 1 otherwise. This is a Bernoulli factory for exp(−λ), and corresponds to g(i) being the Poisson probabilities, namely 1/(i!*exp(1)), and hi() being (1−λ)i.
  3. Generate X, a Poisson random variate with mean μ, run the algorithm for exp(−z) with z = X, and return the result. The probability of returning 1 this way is E[exp(−X)], or exp(μ*exp(−1)−μ). The following Python code uses the computer algebra library SymPy to find this probability: from sympy.stats import *; E(exp(-Poisson('P', x))).simplify().
  4. Bernoulli Race (Dughmi et al. 2021)[^33]: Say we have n coins, then choose one of them uniformly at random and flip that coin. If the flip returns 1, return X; otherwise, repeat this algorithm. This algorithm chooses a random coin based on its probability of heads. Each iteration corresponds to g(i) being 1/n and hi() being the probability for the corresponding coin i.
  5. Multivariate Bernoulli factory (Huber 2016)[^34] of the form R = C0*λ0 + C1*λ1 + ... + Cm−1*λm−1, where Ci are known constants greater than 0, ϵ > 0, and R ≤ 1 − ϵ: Choose an integer in [0, m) uniformly at random, call it i, then run a linear Bernoulli factory for (m*Ci)*λi. This differs from Huber's suggestion of "thinning" a Poisson process driven by multiple input coins.
  6. Probability generating function (PGF) (Dughmi et al. 2021)[^33]. Generates heads with probability E[λX], that is, the expected value ("long-run average") of λX. E[λX] is the PGF for the distribution of X. The algorithm follows: (1) Generate a random integer X in some way; (2) Flip the input coin until the flip returns 0 or the coin is flipped X times, whichever comes first. Return 1 if all the coin flips, including the last, returned 1 (or if X is 0); or return 0 otherwise.
  7. Assume X is the number of unbiased random bits that show 0 before the first 1 is generated. Then g(n) = 1/(2n+1).

Generalized Bernoulli Race

The Bernoulli factory approach, which simulates a coin with unknown heads probability, leads to an algorithm to roll an n-face die where the chance of each face is unknown. Here is one such die-rolling algorithm (Schmon et al. 2019)[^35]. It generalizes the Bernoulli Race from the "Convex Combinations" section, and returns i with probability—

where:

The algorithm follows.

  1. Choose an integer in [0, r] with probability proportional to the following weights: [g(0), g(1), ..., g(r)]. Call the chosen integer i.
  2. Run a Bernoulli factory algorithm for hi(λ). If the run returns 0 (i is rejected), go to step 1. Otherwise (i is accepted), return i.

Notes:

  1. The Bernoulli Race (see "Convex Combinations") is a special case of this algorithm with g(k) = 1 for every k.
  2. If we define S to be a subset of integers in [0, r] and replace step 3 with "If i is in the set S, return 1. Otherwise, return 0.", the algorithm returns 1 with probability ∑k in S φk, and 0 otherwise. In that case, the modified algorithm has the so-called "die-coin algorithm" of Agrawal et al. (2021, Appendix D)[^36] as a special case with—
    g(k) = ck*drk,
    hk(λ, μ) = λk*μrk (for the following algorithm: flip the λ coin k times and the μ coin rk times; return 1 if all flips return 1, or 0 otherwise), and
    S is the closed interval [1, r],
    where c≥0, d≥0, and λ and μ are the probabilities of heads of two input coins. In that paper, c, d, λ, and μ correspond to cy, cx, py, and px, respectively.
  3. Although not noted in the Schmon paper, the r in the algorithm can be infinity (see also Wästlund 1999, Theorem 2.7[^8]). In that case, Step 1 is changed to say "Choose an integer 0 or greater at random with probability g(k) for integer k. Call the chosen integer i." As an example, step 1 can sample from a Poisson distribution, which can take on any integer 0 or greater.

Flajolet's Probability Simulation Schemes

Flajolet et al. (2010)[^1] described two schemes for probability simulation, inspired by restricted models of computing. To describe the schemes, there is a need to generalize the convex combination and generalized Bernoulli race algorithms.

Then both algorithms are now described as follows:

Name To sample: Use this algorithm:
Convex combinations 1 with probability:
$\sum_{k\ge 0} g(k,\lambda) h_k(\lambda)$
(1) Generate a random integer X in some way, using the input coin for λ, such that X is generated with probability g(X, λ).
(2) Flip the coin represented by X (run a Bernoulli factory algorithm for hX(λ)) and return the result.
Generalized Bernoulli race Integer i with probability:
$\frac{g(i,\lambda) h_i(\pmb \mu)}{\sum_{k\ge 0} g(k,\lambda) h_k(\pmb \mu)}$
(μ is the probabilities of heads of one or more input coins.)
(1) Generate a random integer X in some way, using the input coin for λ, such that X is generated with probability proportional to g(X, λ).
(2) Run a Bernoulli factory algorithm for hX(μ). If the run returns 0 (X is rejected), go to step 1. Otherwise (X is accepted), return X.

Note: The probability that $r$ many values of $X$ are rejected by the generalized Bernoulli race is $p(1 − p)^r$, where— $$p=\frac{\sum_{k\ge 0} g(k,\lambda) h_k(\pmb \mu)}{\sum_{k\ge 0} g(k,\lambda)}.$$

Now Flajolet's schemes are described.

Certain algebraic functions. Flajolet et al. (2010)[^1] showed a sampling method modeled on pushdown automata (state machines with a stack) that are given flips of a coin with unknown heads probability λ. These flips form a bitstring, and each pushdown automaton accepts only a certain class of bitstrings. The rules for determining whether a bitstring belongs to that class are called a binary stochastic grammar, which uses an alphabet of only two "letters". If a pushdown automaton terminates, it accepts a bitstring with probability f(λ), where f must be an algebraic function over rationals (a function that can be a solution of a nonzero polynomial equation whose coefficients are rational numbers) (Mossel and Peres 2005)[^15].

Specifically, the method simulates the following function (not necessarily algebraic): $$f(\lambda) = \sum_{k\ge 0} g(k,\lambda) h_k(\lambda),$$ where the paper uses $g(k, \lambda) = \lambda^k (1-\lambda)$ and $h_k(\lambda) = W(k)/\beta^k$, so that— $$f(\lambda) = (1-\lambda) OGF(\lambda/\beta),$$ where:

The method uses the convex combination algorithm given above, where step 1 is done as follows: "Flip the input coin repeatedly until it returns 0. Set X to the number of times the coin returned 1 this way."[^37] Optionally, step 2 can be done as described in Flajolet et al., (2010)[^1]: generate an X-letter word uniformly at random and "parse" that word using a stochastic grammar to determine whether that word can be produced by that grammar.

Note: The radius of convergence of OGF is the greatest number ρ such that OGF is defined at every point less than ρ away from the origin (0, 0). In this algorithm, the radius of convergence is in the interval [1/β, 1] (Flajolet 1987)[^38]. For example, the OGF involved in the square root construction given in the examples below has radius of convergence 1/2.

Examples:

  1. The following is an example from the Flajolet et al. paper. An X-letter binary word can be "parsed" as follows to determine whether that word encodes a ternary tree: "2. If X is 0, return 0. Otherwise, set i to 1 and d to 1.; 2a. Generate an unbiased random bit (that is, either 0 or 1, chosen with equal probability), then subtract 1 from d if that bit is 0, or add 2 to d otherwise.; 2b. Add 1 to i. Then, if i < X and d > 0, go to step 3a.; 2c. Return 1 if d is 0 and i is X, or 0 otherwise."
  2. If W(X), the number of X-letter words that can be produced by the stochastic grammar in question, has the form—

    • choose(X, X/t) * (β−1)XX/t (the number of X-letter words with exactly X/t A's, for an alphabet size of β) if X is divisible by t[^39], and
    • 0 otherwise,

    where t is an integer 2 or greater and β is the alphabet size and is an integer 2 or greater, step 2 of the algorithm can be done as follows: "2. If X is not divisible by t, return 0. Otherwise, generate X uniform random integers in the interval [0, β) (for example, X unbiased random bits if β is 2), then return 1 if exactly X/t zeros were generated this way, or 0 otherwise." If β = 2, then this reproduces another example from the Flajolet paper.

  3. If W(X) has the form—
        choose(X * α, X) * (β−1)X*α−X / βX*α−X,
    where α is an integer 1 or greater and β is the alphabet size and is an integer 2 or greater [^40], step 2 of the algorithm can be done as follows: "2. Generate X * α uniform random integers in the interval [0, β) (for example, X * α unbiased random bits if β is 2), then return 1 if exactly X zeros were generated this way, or 0 otherwise." If α = 2 and β = 2, then this expresses the square-root construction sqrt(1 − λ), mentioned in the Flajolet et al. paper. If α is 1, the modified algorithm simulates the following probability: (β*(λ−1))/(λβ). And interestingly, I have found that if α is 2 or greater, the probability simplifies to involve a hypergeometric function. Specifically, the probability becomes— $$f(\lambda)=(1-\lambda)\times_{\alpha-1} F_{\alpha-2} \left(\frac{1}{\alpha},\frac{2}{\alpha},...,\frac{\alpha-1}{\alpha}; \frac{1}{\alpha-1},\frac{2}{\alpha-1},...,\frac{\alpha-2}{\alpha-1}; \lambda\frac{\alpha^\alpha}{(\alpha-1)^{\alpha-1}2^{\alpha}}\right),$$ if β = 2, or more generally— $$f(\lambda)=(1-\lambda)\times_{\alpha-1} F_{\alpha-2} \left(\frac{1}{\alpha},\frac{2}{\alpha},...,\frac{\alpha-1}{\alpha}; \frac{1}{\alpha-1},\frac{2}{\alpha-1},...,\frac{\alpha-2}{\alpha-1}; \lambda\frac{\alpha^\alpha(\beta-1)^{\alpha-1}}{(\alpha-1)^{\alpha-1}\beta^{\alpha}}\right).$$

    The ordinary generating function for this modified algorithm is thus— $$OGF(z) = 1\times_{\alpha-1} F_{\alpha-2} \left(\frac{1}{\alpha},\frac{2}{\alpha},...,\frac{\alpha-1}{\alpha}; \frac{1}{\alpha-1},\frac{2}{\alpha-1},...,\frac{\alpha-2}{\alpha-1}; z\frac{\alpha^\alpha (\beta-1)^{\alpha-1}}{(\alpha-1)^{\alpha-1}\beta^{\alpha-1}}\right).$$

  4. The probability involved in example 2 likewise involves hypergeometric functions: $$f(\lambda)=(1-\lambda)\times_{t-1} F_{t-2}\left(\frac{1}{t},\frac{2}{t},...,\frac{t-1}{t}; \frac{1}{t-1},\frac{2}{t-1},...,\frac{t-2}{t-1}; \lambda^t \frac{t^t (\beta-1)^{t-1}}{(t-1)^{t-1} \beta^t}\right).$$

The von Neumann schema. Flajolet et al. (2010)[^1], section 2, describes what it calls the von Neumann schema, which produces random integers based on a coin with unknown heads probability. To describe the schema, the following definition is needed:

Now, given a permutation class and an input coin, the von Neumann schema generates a random integer $n\ge 1$, with probability equal to— $$w_n(\lambda) = \frac{g(n,\lambda) h_n(\lambda)}{\sum_{k\ge 0} g(k,\lambda) h_k(\lambda)},$$ where the schema uses $g(k, \lambda) = \lambda^k (1-\lambda)$ and $h_k(\lambda) = \frac{V(k)}{k!}$, so that— $$w_n(\lambda)=\frac{(1-\lambda) \lambda^n V(n)/(n!)}{(1-\lambda) EGF(\lambda)} = \frac{\lambda^n V(n)/(n!)}{EGF(\lambda)},$$ where:

The von Neumann schema uses the generalized Bernoulli race algorithm given earlier in this section. But in step 1, the von Neumann schema as given in the Flajolet paper does the following: "Flip the input coin repeatedly until it returns 0. Set X to the number of times the coin returned 1 this way."[^37] Optionally, step 2 can be implemented as described in Flajolet et al., (2010)[^1]: generate X uniform(0, 1) random variates, then determine whether those numbers satisfy the given permutation class, or generate as many of those numbers as necessary to make this determination.

Note: The von Neumann schema can sample from any power series distribution (such as Poisson, negative binomial, geometric [^41], and logarithmic series), given a suitable exponential generating function. However, the number of input coin flips required by the schema grows without bound as λ approaches 1.

Examples:

  1. Examples of permutation classes include the following (using the notation in "Analytic Combinatorics" (Flajolet and Sedgewick 2009)[^42]):

    • Single-cycle permutations, or permutations whose highest number appears first (EGF(λ) = Cyc(λ) = ln(1/(1 − λ)); V(n) = ((n − 1)!) [or 0 if n is 0)]).
    • Sorted permutations, or permutations whose numbers are sorted in descending order (EGF(λ) = Set(λ) = exp(λ); V(n) = 1).
    • All permutations (EGF(λ) = Seq(λ) = 1/(1 − λ); V(n) = n!),
    • Alternating permutations of even size (EGF(λ) = 1/cos(λ) = sec(λ); V(n) = W(n/2) if n is even[^43] and 0 otherwise, where the W(m) starting at m = 0 is A000364 in the On-Line Encyclopedia of Integer Sequences).
    • Alternating permutations of odd size (EGF(λ) = tan(λ); V(n) = W((n+1)/2) if n is odd[^44] and 0 otherwise, where the W(m) starting at m = 1 is A000182).
  2. Using the class of sorted permutations, we can generate a Poisson random variate with mean λ via the von Neumann schema, where λ is the probability of heads of the input coin. This would lead to an algorithm for exp(−λ) — outputting 1 if a Poisson random variate with mean λ is 0, or 0 otherwise — but for the reason given in the note, this algorithm gets slower as λ approaches 1. Also, if c > 0 is a real number, adding a Poisson random variate with mean floor(c) to one with mean c−floor(c) generates a Poisson random variate with mean c.

  3. The algorithm for exp(−λ), described in example 2, is as follows:

    1. Flip the input coin repeatedly until it returns 0. Set X to the number of times the coin returned 1 this way.
    2. With probability 1/((X)!), X is accepted so return a number that is 1 if X is 0 and 0 otherwise. Otherwise, go to the previous step.
  4. For the class of alternating permutations of even size (see example 1), step 2 can be implemented as follows (Flajolet et al. 2010, sec. 2.2)[^1]:

    • (2a.) (Limit to even-sized permutations.) If X is odd[^44], reject X (and go to step 1).
    • (2b.) Generate a uniform(0, 1) random variate U, then set i to 1.
    • (2c.) While i is less than X:
      • Generate a uniform(0, 1) random variate V.
      • If i is odd[^44] and V is less than U, or if i is even[^43] and U is less than V, reject X (and go to step 1).
      • Add 1 to i, then set U to V.
    • (2d.) (X is accepted.) Return X.
  5. For the class of alternating permutations of odd size (see example 1), step 2 can be implemented as in example 4, except 2a reads: "(2a.) (Limit to odd-sized permutations.) If X is even[^43], reject X (and go to step 1)." (Flajolet et al. 2010, sec. 2.2)[^1].

  6. By computing— $$\frac{\sum_{k\ge 0} g(2k+1,\lambda) h_{2k+1}(\lambda)}{\sum_{k\ge 0} g(k,\lambda) h_k(\lambda)}$$ (which is the probability of getting an odd-numbered output), and using the class of sorted permutations ($h_i(\lambda)=1/(i!)$), we find that the algorithm's output is odd with probability $\exp(-\lambda)\times \sinh(\lambda)$.
  7. The X generated in step 1 can follow any distribution of integers 0 or greater, not just the distribution used by the von Neumann schema (because the Bernoulli race algorithm is more general than the von Neumann schema). (In that case, the function $g(k, \lambda)$ will be the probability of getting $k$ under the new distribution.) For example, if X is a Poisson random variate with mean z2/4, where z > 0, and if the sorted permutation class is used, the algorithm will return 0 with probability 1/I0(z), where I0(.) is the modified Bessel function of the first kind.

Recap. As can be seen—

and both schemes implement step 1 in the same way. However, different choices for $g$ and $h$ will lead to modified schemes that could lead to Bernoulli factory algorithms for new functions.

Integrals

Roughly speaking, the integral of f(x) on an interval [a, b] is the area under that function's graph when the function is restricted to that interval.

Algorithm 1. (Flajolet et al., 2010)[^1] showed how to turn an algorithm that simulates f(λ) into an algorithm that simulates the probability—

namely the following algorithm:

  1. Generate a uniform(0, 1) random variate u.
  2. Create an input coin that does the following: "Flip the original input coin, then sample from the number u. Return 1 if both the call and the flip return 1, and return 0 otherwise."
  3. Run the original Bernoulli factory algorithm, using the input coin described in step 2 rather than the original input coin. Return the result of that run.

Algorithm 2. A special case of Algorithm 1 is the integral $\int_0^1 f(u) du$, when the original input coin always returns 1:

  1. Generate a uniform(0, 1) random variate u.
  2. Create an input coin that does the following: "Sample from the number u and return the result."
  3. Run the original Bernoulli factory algorithm, using the input coin described in step 2 rather than the original input coin. Return the result of that run.

Algorithm 3. I have found that it's possible to simulate the following integral, namely— $$\int_a^b f(\lambda u) du,$$ where $0\le a\lt b\le 1$, using the following algorithm:

  1. Generate a uniform(0, 1) random variate u. Then if u is less than a or is greater than b, repeat this step. (If u is a uniform PSRN, these comparisons should be done via the URandLessThanReal algorithm.)
  2. Create an input coin that does the following: "Sample from the number u and return the result."
  3. Run the original Bernoulli factory algorithm, using the input coin described in step 2. If the run returns 0, return 0. Otherwise, generate a uniform(0, 1) random variate v and return a number that is 0 if v is less than a or is greater than b, or 1 otherwise.

Note: If a is 0 and b is 1, the probability simulated by this algorithm will be strictly increasing (will keep going up), have a slope no greater than 1, and equal 0 at the point 0.

Algorithms for Specific Functions of λ

This section describes algorithms for specific functions, especially when they have a more convenient simulation than the general-purpose algorithms given earlier. They can be grouped as follows:

exp(−λ)

This function can be rewritten as a power series expansion. To simulate it, use the general martingale algorithm (see "Certain Power Series"), with $g(\lambda)=\lambda$, and with $d_0 = 1$ and coefficients $a_i = (-1)^i/(i!)$.[^45]

Note: exp(−λ) = exp(1−λ)/exp(1).

exp(−(λk * c))

In the algorithms in this section, k is an integer 0 or greater, and c ≥ 0 is a real number.

Algorithm 1. Works when c is 0 or greater. (See also algorithm for exp(−((1−λ)1 * c)) in "Other Factory Functions".)

  1. Special case: If c is 0, return 1. If k is 0, run the algorithm for exp(−z) (given later in this page) with z = c, and return the result.
  2. Generate N, a Poisson random variate with mean c.
  3. Set i to 0, then while i < N:
    1. Flip the input coin until the flip returns 0 or the coin is flipped k times, whichever comes first. Return 0 if all of the coin flips (including the last) return 1.
    2. Add 1 to i.
  4. Return 1.

Algorithm 2. The target function can be rewritten as a power series expansion. However, the following algorithm works only when c is a rational number in the interval [0, 1].

  1. Special cases: If c is 0, return 1. If k is 0, run the algorithm for exp(−x/y) (given later in this page) with x/y = c, and return the result.
  2. Run the general martingale algorithm (see "Certain Power Series"), with $g(\lambda) = \lambda^k$, and with parameter $d_0 = 1$ and coefficients $a_i = \frac{(-1)^i c^i}{i!}$, and return the result of that algorithm. (To simulate $\lambda^k$, flip the input coin $k$ times and return either 1 if all the flips return 1, or 0 otherwise.)

Algorithm 3. Builds on Algorithm 2 and works when c is a rational number 0 or greater.

  1. Let m be floor(c). Call the second algorithm m times with k = k and c = 1. If any of these calls returns 0, return 0.
  2. If c is an integer (that is, if floor(c) = c), return 1.
  3. Call the second algorithm once, with k = k and c = c − floor(c). Return the result of this call.

exp(−(m + λ)*μ)

In the following algorithm, m is an integer 0 or greater, and λ and μ are the probabilities of heads of two input coins.

  1. Set j to 0, then while j < m+1:
    1. Generate N, a Poisson random variate with mean 1.
    2. If j = m, flip the λ input coin N times and set N to the number of flips that return 1 this way. (This transforms a Poisson variate with mean 1 to one with mean λ; see (Devroye 1986, p. 487)[^23].)
    3. Flip the μ input coin until a flip returns 1 or the coin is flipped N times, whichever comes first. Return 0 if any of the flips, including the last, returns 1.
    4. Add 1 to j.
  2. Return 1.

exp(−(m + λ)k)

In the following algorithm, m and k are both integers 0 or greater unless noted otherwise.

  1. If k is 0, run the algorithm for exp(−x/y) (given later on this page) with x/y = 1/1, and return the result.
  2. If k is 1 and m is 0, run the algorithm for exp(−λ) and return the result.
  3. If k is 1 and m is greater than 0 (and in this case, m can be any rational number):
    • Run the algorithm for exp(−x/y) with x/y = m. If the algorithm returns 0, return 0. Otherwise, return the result of the algorithm for exp(−λ).
  4. Run the algorithm for exp(−x/y) with x/y = mk / 1. If the algorithm returns 0, return 0.
  5. Run the algorithm for exp(−(λk * c)), with k = k and x = 1. If the algorithm returns 0, return 0.
  6. If m is 0, return 1.
  7. Set i to 1, then while i < k:
    1. Set z to choose(k, i) * mki.
    2. Run the algorithm for exp(−(λk * c)) z times, with k = i and x = 1. If any of these calls returns 0, return 0.
    3. Add 1 to i.
  8. Return 1.

exp(λ)*(1−λ)

(Flajolet et al., 2010)[^1]:

  1. Set k and w each to 0.
  2. Flip the input coin. If it returns 0, return 1.
  3. Generate a uniform(0, 1) random variate U.
  4. If k > 0 and w is less than U, return 0.
  5. Set w to U, add 1 to k, and go to step 2.

(1 − exp(−(m + λ))) / (m + λ)

In this algorithm, m is an integer 0 or greater.

  1. (Write as series expansion if m is 0.) If m is 0, run the general martingale algorithm (see "Certain Power Series"), with $g(\lambda)=\lambda$, and with $d_0 = 1$ and coefficients $a_i = \frac{(-1)^i}{(i+1)!}$, and return the result of that algorithm.
  2. (Separate as two functions otherwise.) If m is greater than 0:
    1. Run the algorithm for exp(−(m + λ)k) with k=1. If it returns 1, return 0.
    2. Run the algorithm for d/(c+λ) with d=1 and c=m, and return the result of that algorithm.

1/(2k + λ) or exp(−(k + λ)*ln(2))

This new algorithm uses the base-2 logarithm k + λ, where k is an integer 0 or greater, and is useful when this logarithm is very large.

  1. If k > 0, generate unbiased random bits until a zero bit or k bits were generated this way, whichever comes first. If a zero bit was generated this way, return 0.
  2. Create an input coin μ that does the following: "Flip the input coin, then run the algorithm for ln(1+y/z) (given later) with y/z = 1/1. If both the call and the flip return 1, return 1. Otherwise, return 0."
  3. Run the algorithm for exp(−μ) using the μ input coin, and return the result.

1/(2m*(k + λ)) or 1/((2m)*(k + λ)) or exp(−(k + λ)*ln(2m))

An extension of the previous algorithm. Here, m is an integer greater than 0.

  1. If k > 0, generate unbiased random bits until a zero bit or k*m bits were generated this way, whichever comes first. If a zero bit was generated this way, return 0.
  2. Create an input coin μ that does the following: "Flip the input coin, then run the algorithm for ln(1+y/z) (given later) with y/z = 1/1. If both the call and the flip return 1, return 1. Otherwise, return 0."
  3. Run the algorithm for exp(−μ) m times, using the μ input coin. If any of the calls returns 0, return 0. Otherwise, return 1.

c * λ * β / (β * (c * λ + d * μ) − (β − 1) * (c + d))

This is the general two-coin algorithm of (Gonçalves et al., 2017)[^46] and (Vats et al. 2022)[^47]. It takes two input coins that each output heads (1) with probability λ or μ, respectively. It also takes parameters c and d, each 0 or greater, and β in the interval [0, 1], which is a so-called "portkey" or early rejection parameter (when β = 1, the formula simplifies to c * λ / (c * λ + d * μ)). In Vats et al. (2022)[^47], β, c, d, λ and μ correspond to β, cy, cx, py, and px, respectively, in the "portkey" algorithm, or to β, x, y, x, and y, respectively, in the "flipped portkey" algorithm.

  1. With probability β, go to step 2. Otherwise, return 0. (For example, call ZeroOrOne with β's numerator and denominator, and return 0 if that call returns 0, or go to step 2 otherwise. ZeroOrOne is described in my article on random sampling methods.)
  2. With probability c / (c + d), flip the λ input coin. Otherwise, flip the μ input coin. If the λ input coin returns 1, return 1. If the μ input coin returns 1, return 0. If the corresponding coin returns 0, go to step 1.

c * λ / (c * λ + d) or (c/d) * λ / (1 + (c/d) * λ))

This algorithm, also known as the logistic Bernoulli factory (Huber 2016)[^34], (Morina et al., 2022)[^17], is a special case of the two-coin algorithm above, but this time uses only one input coin.

  1. With probability d / (c + d), return 0.
  2. Flip the input coin. If the flip returns 1, return 1. Otherwise, go to step 1.

Note: Huber (2016) specifies this Bernoulli factory in terms of a Poisson point process, which seems to require much more randomness on average.

(d + λ) / c

In this algorithm, d and c must be integers, and 0 ≤ d < c.

  1. Generate an integer in [0, c) uniformly at random, call it i.
  2. If i < d, return 1. If i = d, flip the input coin and return the result. If neither is the case, return 0.

d / (c + λ)

In this algorithm, c and d must be rational numbers, c ≥ 1, and 0 ≤ dc. See also the algorithms for continued fractions. (For example, when d = 1, this algorithm can simulate a probability of the form 1 / z, where z is 1 or greater and made up of an integer part (c) and a fractional part (λ) that can be simulated by a Bernoulli factory.)

  1. With probability c / (1 + c), return a number that is 1 with probability d/c and 0 otherwise.
  2. Flip the input coin. If the flip returns 1, return 0. Otherwise, go to step 1.

Note: A quick proof this algorithm works: Let x be the desired probability. Then—
x = (c / (1 + c)) * (d/c) +
(1−c / (1 + c)) * (λ*0 + (1−λ)*x),
and solving for x leads to x=d/(c+λ).

(d + μ) / (c + λ)

Combines the algorithms in the previous two sections.

In this algorithm, c and d must be integers, and 0 ≤ d < c.

  1. With probability c / (1 + c), do the following:
    1. Generate an integer in [0, c) uniformly at random, call it i.
    2. If i < d, return 1. If i = d, flip the μ input coin and return the result. If neither is the case, return 0.
  2. Flip the λ input coin. If the flip returns 1, return 0. Otherwise, go to step 1.

(d + μ) / ((d + μ) + (c + λ))

In this algorithm, c and d are integers 0 or greater, and λ and μ are the probabilities of heads of two different input coins. In the intended use of this algorithm, λ and μ are backed by the fractional parts of two uniform PSRNs, and c and d are their integer parts, respectively.

  1. Let D = d and C = c. Run the algorithm for (d + μ) / (c + λ) with λ and μ both being the μ input coin, with d = D+C, and with c = 1+D + C. If the run returns 1:
    1. If c is 0, return 1.
    2. Run the algorithm for (d + μ) / (c + λ) with λ and μ both being the μ input coin, with d = D, and with c = D + C. If the run returns 1, return 1. Otherwise, return 0.
  2. Flip the λ input coin. If the flip returns 1, return 0. Otherwise, go to step 1.

dk / (c + λ)k, or (d / (c + λ))k

In this algorithm, c and d must be rational numbers, c ≥ 1, and 0 ≤ dc, and k must be an integer 0 or greater.

  1. Set i to 0.
  2. If k is 0, return 1.
  3. With probability c / (1 + c), do the following:
    1. With probability d/c, add 1 to i and then either return 1 if i is now k or greater, or abort these substeps and go to step 2 otherwise.
    2. Return 0.
  4. Flip the input coin. If the flip returns 1, return 0. Otherwise, go to step 2.

1/(1+λ)

This algorithm is a special case of the two-coin algorithm of (Gonçalves et al., 2017)[^46] and has bounded expected running time for all λ parameters.[^48]

  1. Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), return 1.
  2. Flip the input coin. If it returns 1, return 0. Otherwise, go to step 1.

Note: In this special case of the two-coin algorithm, β=1, c=1, d=1, old λ equals 1, and μ equals new λ.

1/(2 − λ)

  1. Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), return 1.
  2. Flip the input coin. If it returns 0, return 0. Otherwise, go to step 1.

Note: Can be derived from the previous algorithm by observing that 1/(2 − λ) = 1/(1 + (1 − λ)).

expit(m + λ) or 1−1/(1+exp(m + λ)) or exp(m + λ)/(1+exp(m + λ)) or 1/(1+exp(−(m + λ)))

expit(x), also known as the logistic function, is the probability that a random variate from the logistic distribution is x or less.

In this algorithm, m is an integer and can be positive or not.

expit((m + λ)*μ)

In this algorithm, m is an integer and can be positive or not, and λ and μ are the probabilities of heads of two input coins.

expit(m + λ)*2 − 1 or tanh((m + λ)/2)

In this algorithm, m is an integer 0 or greater, and λ is the probability of heads of an input coin.

Note: Follows from observing that tanh((m+λ)/2) = (d + (1 − μ)) / (c + μ), where μ = exp(−(m+λ)), d = 0, and c = 1.

λ*exp(m + ν) / (λ*exp(m + ν) + (1 − λ)) or λ*exp(m + ν) / (1 + λ*(exp(m + ν) − 1))

In this algorithm:

The algorithm follows:

Note: This is also a special case of the two-coin algorithm, where β=1, c=exp(m + ν), d=1, λ = λ, and μ = 1 − λ.

1 / (1 + (x/y)*λ)

Another special case of the two-coin algorithm. In this algorithm, x/y must be 0 or greater.

  1. If x is 0, flip the μ input coin and return the result.
  2. With probability y/(x+y), flip the μ input coin and return the result.
  3. Flip the input coin. If the flip returns 1, return 0. Otherwise, go to step 2.

Note: In this special case of the two-coin algorithm, β=1, c=1, d=x/y, old λ equals 1, and μ equals new λ.

Example: μ / (1 + (x/y)*λ) (takes two input coins that simulate λ or μ, respectively): Run the algorithm for 1 / (1 + (x/y)*λ) using the λ input coin. If it returns 0, return 0. Otherwise, flip the μ input coin and return the result.

λ + μ

(Nacu and Peres 2005, proposition 14(iii))[^16]. This algorithm takes two input coins that simulate λ or μ, respectively, and a parameter ϵ in the half-open interval (0, 1 − λμ].

  1. Create a ν input coin that does the following: "Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), flip the λ input coin and return the result. Otherwise, flip the μ input coin and return the result."
  2. Run a linear Bernoulli factory using the ν input coin, x/y = 2/1, and ϵ = ϵ, and return the result.

λμ

(Nacu and Peres 2005, proposition 14(iii-iv))[^16]. This algorithm takes two input coins that simulate λ or μ, respectively, and a parameter ϵ in the half-open interval (0, λμ] (the greater ϵ is, the more efficient).

  1. Create a ν input coin that does the following: "Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), flip the λ input coin and return 1 minus the result. Otherwise, flip the μ input coin and return the result."
  2. Run a linear Bernoulli factory using the ν input coin, x/y = 2/1, and ϵ = ϵ, and return 1 minus the result.

ϵ / λ

(Lee et al. 2014)[^51]. This algorithm, in addition to the input coin, takes a parameter ϵ in the half-open interval (0, λ].

  1. Set β to max(ϵ, 1/2) and set γ to 1 − (1 − β) / (1 − (β / 2)).
  2. Create a μ input coin that flips the input coin and returns 1 minus the result.
  3. With probability ϵ, return 1.
  4. Run a linear Bernoulli factory with the μ input coin, x/y = 1 / (1 − ϵ), and ϵ = γ. If the result is 0, return 0. Otherwise, go to step 3. (Running the linear Bernoulli factory this way simulates the probability (λϵ)/(1 − ϵ) or 1 − (1 − λ)/(1 − ϵ)).

μ / λ

(Morina 2021)[^52]. This division algorithm takes two input coins, namely a coin simulating the dividend μ and a coin simulating the divisor λ, and a parameter ϵ in the half-open interval (0, λμ]. In this algorithm, μ must be less than λ.

λx/y

In the algorithm below, the case where x/y is in the open interval (0, 1) is due to Mendo (2019)[^19]. The algorithm works only when x/y is 0 or greater.

  1. If x/y is 0, return 1.
  2. If x/y is equal to 1, flip the input coin and return the result.
  3. If x/y is greater than 1:
    1. Set ipart to floor(x/y) and fpart to rem(x, y) (equivalent to x - y*floor(x/y)).
    2. If fpart is greater than 0, subtract 1 from ipart, then call this algorithm recursively with x = floor(fpart/2) and y = y, then call this algorithm, again recursively, with x = fpart − floor(fpart/2) and y = y. Return 0 if either call returns 0. (This is done rather than the more obvious approach in order to avoid calling this algorithm with fractional parts very close to 0, because the algorithm runs much more slowly than for fractional parts closer to 1.)
    3. If ipart is 1 or greater, flip the input coin ipart many times. Return 0 if any of these flips returns 1.
    4. Return 1.
  4. x/y is less than 1, so set i to 1.
  5. Do the following process repeatedly, until this algorithm returns a value:
    1. Flip the input coin; if it returns 1, return 1.
    2. With probability x/(y*i), return 0. (Note: x/(y*i) = (x/y) * (1/i).)
    3. Add 1 to i.

Notes:

  1. When x/y is less than 1, the expected number of flips grows without bound as λ approaches 0. In fact, no fast Bernoulli factory algorithm can avoid this unbounded growth without additional information on λ (Mendo 2019)[^19].
  2. Another algorithm is discussed in the online community Cross Validated.

λμ

This algorithm is based on the previous one, but changed to accept a second input coin (which outputs heads with probability μ) rather than a fixed value for the exponent.

sqrt(λ)

Special case of the previous algorithm with μ = 1/2.

λ * x/y

In general, this function will touch 0 or 1 somewhere in the open interval (0, 1), when x/y > 1. This makes the function relatively non-trivial to simulate in this case.

Huber has suggested several algorithms for this function over the years.

The first algorithm in this document comes from Huber (2014)[^4]. It uses three parameters:

As a result, some knowledge of λ has to be available to the algorithm. The algorithm as described below also includes certain special cases, not mentioned in Huber, to make it more general.

  1. Special cases: If x is 0, return 0. Otherwise, if x equals y, flip the input coin and return the result. Otherwise, if x is less than y, then do the following: "With probability x/y, flip the input coin and return the result; otherwise return 0."
  2. Set c to x/y, and set k to 23 / (5 * ϵ).
  3. If ϵ is greater than 644/1000, set ϵ to 644/1000.
  4. Set i to 1.
  5. While i is not 0:
    1. Flip the input coin. If it returns 0, then generate numbers that are each 1 with probability (c − 1) / c and 0 otherwise, until 1 is generated this way, then add 1 to i for each number generated this way (including the last).
    2. Subtract 1 from i.
    3. If i is k or greater:
      1. Generate i numbers that are each 1 with probability 2 / (ϵ + 2) or 0 otherwise. If any of those numbers is 0, return 0.
      2. Multiply c by 2 / (ϵ + 2), then divide ϵ by 2, then multiply k by 2.
  6. (i is 0.) Return 1.

Huber (2016)[^34] presented a second algorithm using the same three parameters, but it's omitted here because it appears to perform worse than the algorithm given above and the algorithm for (λ * x/y)i below (see also Morina 2021[^52]).

Huber (2016) also included a third algorithm that simulates λ * x / y. The algorithm works only if λ * x / y is known to be less than 1/2. This third algorithm takes three parameters:

The algorithm follows.

  1. The same special cases as for the first algorithm in this section apply.
  2. Run the logistic Bernoulli factory algorithm with c/d = (x/y) / (1 − 2 * m). If it returns 0, return 0.
  3. With probability 1 − 2 * m, return 1.
  4. Run a linear Bernoulli factory with x/y = (x/y) / (2 * m) and ϵ = 1 − m.

Note: For approximate methods to simulate λ*(x/y), see the page "Supplemental Notes for Bernoulli Factory Algorithms".

(λ * x/y)i

(Huber 2019)[^53]. This algorithm uses four parameters:

The algorithm also has special cases not mentioned in Huber 2019.

  1. Special cases: If i is 0, return 1. If x is 0, return 0. Otherwise, if x equals y and i equals 1, flip the input coin and return the result.
  2. Special case: If x is less than y and i = 1, then do the following: "With probability x/y, flip the input coin and return the result; otherwise return 0."
  3. Special case: If x is less than y, then create a secondary coin that does the following: "With probability x/y, flip the input coin and return the result; otherwise return 0", then run the algorithm for λx/y with x=i, y=1, and λ being the secondary coin, then return the result of that run.
  4. Set t to 355/100 and c to x/y.
  5. While i is not 0:
    1. While i > t / ϵ:
      1. Set β to (1 − ϵ / 2) / (1 − ϵ).
      2. Run the algorithm for (a/b)x/y (given in the irrational constants section) with a=1, b=β, x=i, and y=1. If the run returns 0, return 0.
      3. Multiply c by β, then divide ϵ by 2.
    2. Run the logistic Bernoulli factory with c/d = c, then set z to the result. Set i to i + 1 − z * 2.
  6. (i is 0.) Return 1.

Linear Bernoulli Factories

In this document, a linear Bernoulli factory refers to one of the following:

arctan(λ) /λ

Based on the algorithm from Flajolet et al. (2010)[^1], but uses the two-coin algorithm (which has bounded expected running time for every λ parameter) rather than the even-parity construction (which is not).[^54]

arctan(λ)

(Flajolet et al., 2010)[^1]: Call the algorithm for arctan(λ) /λ and flip the input coin. Return 1 if the call and flip both return 1, or 0 otherwise.

cos(λ)

This function can be rewritten as a power series expansion. To simulate it, use the general martingale algorithm (see "Certain Power Series"), with $g(\lambda)=\lambda$, and with $d_0 = 1$ and coefficients $a_i = (-1)^{i/2} / (i!)$ if $i$ is even[^43] and 0 otherwise.

sin(λ*sqrt(c)) / (λ*sqrt(c))

This function can be rewritten as a power series expansion. To simulate it, use the general martingale algorithm (see "Certain Power Series"), with $g(\lambda)=\lambda$, and with $d_0 = 1$ and coefficients $a_i = \frac{ (-1)^{i/2} c^{i/2}}{(i+1)!}$ if $i$ is even[^43] and 0 otherwise. In this algorithm, c must be a rational number in the interval (0, 6].

sin(λ)

Equals the previous function times λ, with c = 1.

(1−λ)/cos(λ)

(Flajolet et al., 2010)[^1]. Uses an average number of flips that grows without bound as λ goes to 1.

  1. Flip the input coin until the flip returns 0. Then set G to the number of times the flip returns 1 this way.
  2. If G is odd, return 0.
  3. Generate a uniform(0, 1) random variate U, then set i to 1.
  4. While i is less than G:
    1. Generate a uniform(0, 1) random variate V.
    2. If i is odd[^44] and V is less than U, return 0.
    3. If i is even[^43] and U is less than V, return 0.
    4. Add 1 to i, then set U to V.
  5. Return 1.

(1−λ) * tan(λ)

(Flajolet et al., 2010)[^1]. Uses an average number of flips that grows without bound as λ goes to 1.

  1. Flip the input coin until the flip returns 0. Then set G to the number of times the flip returns 1 this way.
  2. If G is even, return 0.
  3. Generate a uniform(0, 1) random variate U, then set i to 1.
  4. While i is less than G:
    1. Generate a uniform(0, 1) random variate V.
    2. If i is odd[^44] and V is less than U, return 0.
    3. If i is even[^43] and U is less than V, return 0.
    4. Add 1 to i, then set U to V.
  5. Return 1.

ln(1+λ)

Based on the algorithm from Flajolet et al. (2010)[^1], but uses the two-coin algorithm (which has bounded expected running time for every λ parameter) rather than the even-parity construction (which is not).[^55]

ln((c + d + λ)/c)

In this algorithm, d and c are integers, 0 < c < d, and d ≥ 0, and (c + d + λ)/c ≤ exp(1).

arcsin(λ) + sqrt(1 − λ2) − 1

(Flajolet et al., 2010)[^1]. The algorithm given here uses the two-coin algorithm rather than the even-parity construction.

  1. Generate a uniform(0, 1) random variate u.
  2. Create a secondary coin μ that does the following: "Sample from the number u twice, and flip the input coin twice. If all of these calls and flips return 1, return 0. Otherwise, return 1."
  3. Call the algorithm for μ1/2 using the secondary coin μ. If it returns 0, return 0.
  4. Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), flip the input coin and return the result.
  5. Sample from the number u once, and flip the input coin once. If both the call and flip return 1, return 0. Otherwise, go to step 4.

arcsin(λ) / 2

The Flajolet paper doesn't explain in detail how arcsin(λ)/2 arises out of arcsin(λ) + sqrt(1 − λ2) − 1 via Bernoulli factory constructions, but here is an algorithm.[^56] However, the number of input coin flips is expected to grow without bound as λ approaches 1.

  1. Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), run the algorithm for arcsin(λ) + sqrt(1 − λ2) − 1 and return the result.
  2. Create a secondary coin μ that does the following: "Flip the input coin twice. If both flips return 1, return 0. Otherwise, return 1." (The coin simulates 1 − λ2.)
  3. Call the algorithm for μ1/2 using the secondary coin μ. If it returns 0, return 1; otherwise, return 0. (This step effectively cancels out the sqrt(1 − λ2) − 1 part and divides by 2.)

tanh(m + λ)

In this algorithm, m is an integer 0 or greater, and λ is the probability of heads of an input coin.[^57]

Note: Follows from observing that tanh(m+λ) = (d + (1 − μ)) / (c + μ), where μ = (exp(−(m+λ)))2, d = 0, and c = 1.

Expressions Involving Polylogarithms

The following algorithm simulates the expression Lir(λ) * (1 / λ − 1), where Lir(.) is a polylogarithm of order r, and r is an integer 1 or greater. However, even with a relatively small r such as 6, the expression quickly approaches a straight line.

If λ is 1/2, this expression simplifies to Lir(1/2). See also (Flajolet et al., 2010)[^1]. See also "Convex Combinations" (the case of 1/2 works by decomposing the series forming the polylogarithmic constant into g(i) = (1/2)i, which sums to 1, and hi() = 1/ir, where i ≥ 1).

  1. Flip the input coin until it returns 0, and let t be 1 plus the number of times the coin returned 1 this way.
  2. Return a number that is 1 with probability 1/tr and 0 otherwise.

Other Factory Functions

Algorithms in bold are given in this page.

To simulate: Follow this algorithm:
1/sqrt(π) Create λ coin for algorithm 1/π.
Run algorithm for sqrt(λ).
1/sqrt(h+λ) (λ is unknown heads probability of a coin; h≥1 is a rational number.)
Create μ coin for algorithm d/(c+λ) with c=h and d=1.
Run algorithm for sqrt(λ) with λ being the μ coin.
1 / (c + λ) (λ is unknown heads probability of a coin; c≥1 is a rational number.)
Run algorithm for d / (c + λ) with d = 1.
1 / (1 + λ2) (Slope function of arctan(λ). λ is unknown heads probability of a coin.)
Create μ coin that flips λ coin twice and returns either 1 if both flips return 1, or 0 otherwise.
Run algorithm for d / (c + λ) with d=1, c=1, and λ being the μ coin.
1 / (c + exp(− λ)) (λ is unknown heads probability of a coin; c≥1 is a rational number.)
Create μ coin for algorithm exp(− λ).
Run algorithm for d / (c + λ) with d=1, c=c, and λ being the μ coin.
1−exp(− (_m + λ)) = (exp((m+λ))−1) * exp(−(m+λ)) = (exp(m+λ)−1) / exp(m+λ) (λ is unknown heads probability of a coin. m ≥ 0 is a rational number.)
Run algorithm exp(−(m+λ)k) with k = 1, and return 1 minus the result.
exp(−((1−λ)1 * c)) ((Dughmi et al. 2021)[^33]; applies an exponential weight—here, c—to an input coin)
(1) If c is 0, return 1.
(2) Generate N, a Poisson random variate with mean c.
(3) Flip the input coin until the flip returns 0 or the coin is flipped N times, whichever comes first, then return a number that is 1 if N is 0 or all of the coin flips (including the last) return 1, or 0 otherwise.
exp(λ)*(1−λ) (λ is unknown heads probability of a coin.)
Run algorithm for power series 1, with c[i] = (i−1)/(i!), and CS = 1 (see "Certain Power Series).
exp(λ2) − λ*exp(λ2) (λ is unknown heads probability of a coin.)
Run general martingale algorithm with $g(\lambda)=\lambda$, $d_0=1$, and $a_i=\frac{(-1)^i}{(\text{floor}(i/2))!}$.
1 − 1 / (1+(μ*λ/(1 − μ)) =
(μ*λ/(1 − μ) / (1+(μ*λ/(1 − μ))
(Special case of logistic Bernoulli factory; λ is in [0, 1], μ is in [0, 1), and both are unknown heads probabilities of two coins.)
(1) Flip the μ coin. If it returns 0, return 0. (Coin samples probability μ/(μ + (1 − μ)) = μ.)
(2) Flip the λ coin. If it returns 1, return 1. Otherwise, go to step 1.
λ/(1+λ) (λ is unknown heads probability of a coin.)
Run algorithm for 1/(1+λ), then return 1 minus the result.
exp(m + λ)/(1+exp(m + λ))2 (Equals expit(m + λ)*(1−expit(m + λ)). λ is unknown heads probability of a coin.)
Run the algorithm for expit(m + λ) twice, with m=0. If the first run returns 1 and the second returns 0, return 1. Otherwise, return 0.
ν * 1 + (1 − ν) * μ = ν + μ − (ν*μ) (Logical OR. Flajolet et al., 2010[^1]. Special case of ν * λ + (1 − ν) * μ with λ = 1. ν and μ are unknown heads probabilities of two coins.)
Flip the ν input coin and the μ input coin. Return 1 if either flip returns 1, and 0 otherwise.
1 − ν (Complement. Flajolet et al., 2010[^1]. Special case of ν * λ + (1 − ν) * μ with λ = 0 and μ = 1. ν is unknown heads probability of a coin.)
Flip the ν input coin and return 1 minus the result.
ν * λ (Logical AND or Product. Flajolet et al., 2010[^1]. Special case of ν * λ + (1 − ν) * μ with μ = 0. ν and λ are unknown heads probabilities of two coins.)
Flip the ν input coin and the λ input coin. Return 1 if both flips return 1, and 0 otherwise.
(λ + μ)/2 = (1/2)*λ + (1/2)*μ (Mean. Nacu and Peres 2005, proposition 14(iii)[^16]; Flajolet et al., 2010[^1]. Special case of ν * λ + (1 − ν) * μ with ν = 1/2. λ and μ are unknown heads probabilities of two coins.)
Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), flip the λ input coin and return the result. Otherwise, flip the μ input coin and return the result.
(1+λ)/2 = (1/2) + (1/2)*λ (λ is unknown heads probability of a coin.)
Generate an unbiased random bit. If that bit is 1, return 1. Otherwise, flip the input coin and return the result.
(1−λ)/2 (λ is unknown heads probability of a coin.)
Generate an unbiased random bit. If that bit is 1, return 0. Otherwise, flip the input coin and return 1 minus the result.
1 − ln(1+λ) (λ is unknown heads probability of a coin.)
Run algorithm for ln(1+λ), then return 1 minus the result.[^58]
sin(sqrt(λ)*sqrt(c)) / (sqrt(λ)*sqrt(c)) (c is a rational number in (0, 6]. λ is unknown heads probability of a coin.)
Run general martingale algorithm with $g(\lambda)=\lambda$, and with $d_0 = 1$ and coefficients $a_i = \frac{ (-1)^{i} c^{i}}{(i+i+1)!}$.

Algorithms for Specific Constants

This section shows algorithms to simulate a probability equal to a specific kind of irrational number.

1 / φ (1 divided by the golden ratio)

This algorithm uses the algorithm described in the section on continued fractions to simulate 1 divided by the golden ratio (about 0.618), whose continued fraction's partial denominators are 1, 1, 1, 1, ....

  1. Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), return 1.
  2. Do a separate run of the currently running algorithm. If the separate run returns 1, return 0. Otherwise, go to step 1.

sqrt(2) − 1

Another example of a continued fraction is that of the fractional part of the square root of 2, where the partial denominators are 2, 2, 2, 2, .... The algorithm to simulate this number is as follows:

  1. With probability 2/3, generate an unbiased random bit and return that bit.
  2. Do a separate run of the currently running algorithm. If the separate run returns 1, return 0. Otherwise, go to step 1.

1/sqrt(2)

This third example of a continued fraction shows how to simulate a probability 1/z, where z > 1 has a known simple continued fraction expansion. In this case, the partial denominators are as follows: floor(z), a[1], a[2], ..., where the a[i] are z's partial denominators (not including z's integer part). In the example of 1/sqrt(2), the partial denominators are 1, 2, 2, 2, ..., where 1 comes first since floor(sqrt(2)) = 1. The algorithm to simulate 1/sqrt(2) is as follows:

The algorithm begins with pos equal to 1. Then the following steps are taken.

  1. If pos is 1, return 1 with probability 1/2. If pos is greater than 1, then with probability 2/3, generate an unbiased random bit and return that bit.
  2. Do a separate run of the currently running algorithm, but with pos = pos + 1. If the separate run returns 1, return 0. Otherwise, go to step 1.

tanh(1/2) or (exp(1) − 1) / (exp(1) + 1)

The algorithm begins with k equal to 2. Then the following steps are taken.

  1. With probability k/(1+k), return a number that is 1 with probability 1/k and 0 otherwise.
  2. Do a separate run of the currently running algorithm, but with k = k + 4. If the separate run returns 1, return 0. Otherwise, go to step 1.

arctan(x/y) * y/x

(Flajolet et al., 2010)[^1]:

  1. Generate a uniform(0, 1) random variate u.
  2. Generate a number that is 1 with probability x * x/(y * y), or 0 otherwise. If the number is 0, return 1.
  3. Sample from the number u twice. If either of these calls returns 0, return 1.
  4. Generate a number that is 1 with probability x * x/(y * y), or 0 otherwise. If the number is 0, return 0.
  5. Sample from the number u twice. If either of these calls returns 0, return 0. Otherwise, go to step 2.

Observing that the even-parity construction used in the Flajolet paper is equivalent to the two-coin algorithm, which has bounded expected running time for all λ parameters, the algorithm above can be modified as follows:

  1. Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), return 1.
  2. Generate a uniform(0, 1) random variate u, if it wasn't generated yet.
  3. With probability x * x/(y * y), sample from the number u twice. If both of these calls return 1, return 0.
  4. Go to step 1.

π / 12

Two algorithms:

π / 4

(Flajolet et al., 2010)[^1]:

  1. Generate a random integer in the interval [0, 6), call it n.
  2. If n is less than 3, return the result of the algorithm for arctan(1/2) * 2. Otherwise, if n is 3, return 0. Otherwise, return the result of the algorithm for arctan(1/3) * 3.

1 / π

(Flajolet et al., 2010)[^1]:

  1. Set t to 0.
  2. With probability 1/4, add 1 to t and repeat this step. Otherwise, go to step 3.
  3. With probability 1/4, add 1 to t and repeat this step. Otherwise, go to step 4.
  4. With probability 5/9, add 1 to t.
  5. Generate 2*t unbiased random bits (that is, either 0 or 1, chosen with equal probability), and return 0 if there are more zeros than ones generated this way or more ones than zeros. (In fact, this condition can be checked even before all the bits are generated this way.) Do this step two more times.
  6. Return 1.

For a sketch of how this algorithm is derived, see the appendix.

(a/b)x/y

In the algorithm below, a, b, x, and y are integers, and the case where x/y is in the open interval (0, 1) is due to recent work by Mendo (2019)[^19]. This algorithm works only if—

The algorithm follows.

  1. If x/y is less than 0, swap a and b, and remove the sign from x/y. If a/b is now less than 0 or greater than 1, return an error.
  2. If x/y is equal to 1, return 1 with probability a/b and 0 otherwise.
  3. If x is 0, return 1. Otherwise, if a is 0, return 0. Otherwise, if a equals b, return 1.
  4. If x/y is greater than 1:
    1. Set ipart to floor(x/y) and fpart to rem(x, y) (equivalent to x - y*floor(x/y)).
    2. If fpart is greater than 0, subtract 1 from ipart, then call this algorithm recursively with x = floor(fpart/2) and y = y, then call this algorithm, again recursively, with x = fpart − floor(fpart/2) and y = y. Return 0 if either call returns 0. (This is done rather than the more obvious approach in order to avoid calling this algorithm with fractional parts very close to 0, because the algorithm runs much more slowly than for fractional parts closer to 1.)
    3. If ipart is 1 or greater, generate at random a number that is 1 with probability aipart/bipart or 0 otherwise. (Or generate, at random, ipart many numbers that are each 1 with probability a/b or 0 otherwise, then multiply them all into one number.) If that number is 0, return 0.
    4. Return 1.
  5. Set i to 1.
  6. With probability a/b, return 1.
  7. Otherwise, with probability x/(y*i), return 0.
  8. Add 1 to i and go to step 6.

exp(−x/y)

This algorithm takes integers x ≥ 0 and y > 0 and outputs 1 with probability exp(-x/y) or 0 otherwise. It originates from (Canonne et al. 2020)[^59].

  1. Special case: If x is 0, return 1. (This is because the probability becomes exp(0) = 1.)
  2. If x > y (so x/y is greater than 1), call this algorithm (recursively) floor(x/y) times with x = y = 1 and once with x = x − floor(x/y) * y and y = y. Return 1 if all these calls return 1; otherwise, return 0.
  3. Set r to 1 and i to 1.
  4. Return r with probability (y * ix) / (y * i).
  5. Set r to 1 − r, add 1 to i, and go to step 4.

exp(−z)

This algorithm is similar to the previous algorithm, except that the exponent, z, can be any real number 0 or greater, as long as z can be rewritten as the sum of one or more components whose fractional parts can each be simulated by a Bernoulli factory algorithm that outputs heads with probability equal to that fractional part. (This makes use of the identity exp(−a) = exp(−b) * exp(−c).)

More specifically:

  1. Decompose z into n > 0 components that sum to z, all of which are greater than 0. For example, if z = 3.5, it can be decomposed into only one component, 3.5 (whose fractional part is trivial to simulate), and if z = π, it can be decomposed into four components that are all (π / 4), which has a not-so-trivial simulation described earlier on this page.
  2. For each component LC[i] found this way (where i is in [1, n]), let LI[i] be floor(LC[i]) and let LF[i] be LC[i] − floor(LC[i]) (LC[i]'s fractional part).

The algorithm is then as follows:

(a/b)z

This algorithm is similar to the previous algorithm for powering, except that the exponent, z, can be any real number 0 or greater, as long as z can be rewritten as the sum of one or more components whose fractional parts can each be simulated by a Bernoulli factory algorithm that outputs heads with probability equal to that fractional part. This algorithm makes use of a similar identity as for exp and works only if z is 0 or greater and a/b is in the interval [0, 1].

Decompose z into LC[i], LI[i], and LF[i] just as for the exp(− z) algorithm. The algorithm is then as follows.

1 / (1 + exp(x / (y * 2prec)) (LogisticExp)

This is the probability that the bit at prec (the precth bit after the point) is set for an exponential random variate with rate x/y. This algorithm is a special case of the logistic Bernoulli factory.

  1. Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), return 1.
  2. Call the algorithm for exp(− x/(y * 2prec)). If the call returns 1, return 1. Otherwise, go to step 1.

1 / (1 + exp(z / 2prec)) (LogisticExp)

This is similar to the previous algorithm, except that z can be any real number described in the algorithm for exp(−z).

Decompose z into LC[i], LI[i], and LF[i] just as for the exp(−z) algorithm. The algorithm is then as follows.

  1. For each component LC[i], create an input coin that does the following: "(a) With probability 1/(2prec), return 1 if the input coin that simulates LF[i] returns 1; (b) Return 0".
  2. Return 0 with probability 1/2.
  3. Call the algorithm for exp(− x/y) with x = LI[1] + LI[2] + ... + LI[n] and y = 2prec. If this call returns 0, go to step 2.
  4. For each component LC[i], call the algorithm for exp(−λ), using the corresponding input coin for LC[i] created in step 1. If any of these calls returns 0, go to step 2. Otherwise, return 1.

ζ(3) * 3 / 4 and Other Zeta-Related Constants

(Flajolet et al., 2010)[^1]. It can be seen as a triple integral of the function 1/(1 + a * b * c), where a, b, and c are uniform(0, 1) random variates. This algorithm is given below, but using the two-coin algorithm instead of the even-parity construction. Here, ζ(x) is the Riemann zeta function.

  1. Generate three uniform(0, 1) random variates.
  2. Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), return 1.
  3. Sample from each of the three numbers generated in step 1. If all three calls return 1, return 0. Otherwise, go to step 2. (This implements a triple integral involving the uniform random variates.)

Note: The triple integral in section 5 of the paper is ζ(3) * 3 / 4, not ζ(3) * 7 / 8.

This can be extended to cover any constant of the form ζ(k) * (1 − 2−(k − 1)) where k ≥ 2 is an integer, as suggested slightly by the Flajolet paper when it mentions ζ(5) * 31 / 32 (which should probably read ζ(5) * 15 / 16 instead), using the following algorithm.

  1. Generate k uniform(0, 1) random variates.
  2. Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), return 1.
  3. Sample from each of the k numbers generated in step 1. If all k calls return 1, return 0. Otherwise, go to step 2.

erf(x)/erf(1)

In the following algorithm, x is a real number in the interval [0, 1].

  1. Generate a uniform(0, 1) random variate, call it ret.
  2. Set u to point to the same value as ret, and set k to 1.
  3. (In this and the next step, we create v, which is the maximum of two uniform [0, 1] random variates.) Generate two uniform(0, 1) random variates, call them a and b.
  4. If a is less than b, set v to b. Otherwise, set v to a.
  5. If v is less than u, set u to v, then add 1 to k, then go to step 3.
  6. If k is odd[^44], return 1 if ret is less than x, or 0 otherwise. (If ret is implemented as a uniform PSRN, this comparison should be done via the URandLessThanReal algorithm, which is described in my article on PSRNs.)
  7. Go to step 1.

In fact, this algorithm takes advantage of a theorem related to the Forsythe method of random sampling (Forsythe 1972)[^60]. See the section "Probabilities Arising from Certain Permutations" in the appendix for more information.

Note: If the last step in the algorithm reads "Return 0" rather than "Go to step 1", then the algorithm simulates the probability erf(x)*sqrt(π)/2 instead.

2 / (1 + exp(2)) or (1 + exp(0)) / (1 + exp(1))

This algorithm takes advantage of formula 2 mentioned in the section "Probabilities Arising from Certain Permutations" in the appendix. Here, the relevant probability is rewritten as 1 − (∫(−∞, 1) (1 − exp(−max(0, min(1, z)))) * exp(−z) dz) / (∫(−∞, ∞) (1 − exp(−max(0, min(1, z))) * exp(−z) dz).

  1. Generate an exponential random variate ex, then set k to 1.
  2. Set u to point to the same value as ex.
  3. Generate a uniform(0,1) random variate v.
  4. Set stop to 1 if u is less than v, and 0 otherwise.
  5. If stop is 1 and k is even, return a number that is 0 if ex is less than 1, and 1 otherwise. Otherwise, if stop is 1, go to step 1.
  6. Set u to v, then add 1 to k, then go to step 3.

(1 + exp(1)) / (1 + exp(2))

This algorithm takes advantage of the theorem mentioned in the section "Probabilities Arising from Certain Permutations" in the appendix. Here, the relevant probability is rewritten as 1 − (∫(−∞, 1/2) exp(−max(0, min(1, z))) * exp(−z) dz) / (∫(−∞, ∞) exp(−max(0, min(1, z)) * exp(−z) dz).

  1. Generate an exponential random variate ex, then set k to 1.
  2. Set u to point to the same value as ex.
  3. Generate a uniform(0,1) random variate v.
  4. Set stop to 1 if u is less than v, and 0 otherwise.
  5. If stop is 1 and k is odd, return a number that is 0 if ex is less than 1/2, and 1 otherwise. Otherwise, if stop is 1, go to step 1.
  6. Set u to v, then add 1 to k, then go to step 3.

(1 + exp(k)) / (1 + exp(k + 1))

In this algorithm, k must be an integer 0 or greater.

  1. If k is 0, run the algorithm for 2 / (1 + exp(2)) and return the result. If k is 1, run the algorithm for (1 + exp(1)) / (1 + exp(2)) and return the result.
  2. Create a μ input coin that runs the sub-algorithm given below.
  3. Run a linear Bernoulli factory using the μ input coin, x=2, y=1, and ϵ = 6/10 (6/10 because it's less than 1 minus (1 + exp(2)) / (1 + exp(2+1))), and return the result of that run.

The sub-algorithm referred to is the following (which simulates the probability (1/(1+exp(k+1)) + exp(k)/(1+exp(k+1)))/2):

  1. Generate an unbiased random bit. If that bit is 1, simulate the probability 1/(1+exp(k+1)) as follows: Do the following process repeatedly, until this algorithm returns a value:
    1. Generate an unbiased random bit. If that bit is 1, return 0.
    2. Run the algorithm for exp(−x/y) with x/y = (k+1)/1. If it returns 1, return 1.
  2. The bit is 0, so simulate the probability exp(k)/(1+exp(k+1)) as follows: Do the following process repeatedly, until this algorithm returns a value:
    1. Generate an unbiased random bit. If that bit is 1, run the algorithm for exp(−x/y) with x/y = 1/1 and return the result.
    2. Run the algorithm for exp(−x/y) with x/y = (k+1)/1. If it returns 1, return 0.

See "More Algorithms for Arbitrary-Precision Sampling" for another way to sample this probability.

Euler–Mascheroni constant γ

The following algorithm to simulate the Euler–Mascheroni constant γ (about 0.5772) is due to Mendo (2020/2021)[^28]. This solves an open question given in (Flajolet et al., 2010)[^1]. The series used was given by Sondow (2005)[^61]. An algorithm for γ appears here even though it is not yet known whether this constant is irrational.

  1. Set ϵ to 1, then set n, lamunq, lam, s, k, and prev to 0 each.
  2. Add 1 to k, then add s/(2k) to lam.
  3. If lamunq+ϵlam + 1/(2k), go to step 8.
  4. If lamunq > lam + 1/(2k), go to step 8.
  5. If lamunq > lam + 1/(2k+1) and lamunq+ϵ < 3/(2k+1), go to step 8.
  6. (This step adds a term of the series for γ to lamunq, and sets ϵ to an upper bound on the error that results if the series is "cut off" after summing this and the previous terms.) If n is 0, add 1/2 to lamunq and set ϵ to 1/2. Otherwise, add B(n)/(2*n*(2*n+1)*(2*n+2)) to lamunq and set ϵ to min(prev, (2+B(n)+(1/n))/(16*n*n)), where B(n) is the minimum number of bits needed to store n (or the smallest integer b≥1 such that n < 2b).
  7. Add 1 to n, then set prev to ϵ, then go to step 3.
  8. Let bound be lam+1/(2k). If lamunq+ϵbound, set s to 0. Otherwise, if lamunq > bound, set s to 2. Otherwise, set s to 1.
  9. Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), go to step 2. Otherwise, return a number that is 0 if s is 0, 1 if s is 2, or an unbiased random bit (either 0 or 1 with equal probability) otherwise.

exp(−x/y) * z/t

This algorithm is again based on an algorithm due to Mendo (2020/2021)[^28]. In this algorithm, x, y, z, and t are integers greater than 0, except x and/or z may be 0, and must be such that exp(−x/y) * z/t is in the interval [0, 1].

  1. If z is 0, return 0. If x is 0, return a number that is 1 with probability z/t and 0 otherwise.
  2. Set ϵ to 1, then set n, lamunq, lam, s, and k to 0 each.
  3. Add 1 to k, then add s/(2k) to lam.
  4. If lamunq+ϵlam + 1/(2k), go to step 9.
  5. If lamunq > lam + 1/(2k), go to step 9.
  6. If lamunq > lam + 1/(2k+1) and lamunq+ϵ < 3/(2k+1), go to step 8.
  7. (This step adds two terms of exp(−x/y)'s alternating series, multiplied by z/t, to lamunq, and sets ϵ to an upper bound on how close the current sum is to the desired probability.) Let m be n*2. Set ϵ to z*xm/(t*(m!)*ym). If m is 0, add z*(yx)/(t*y) to lamunq. Otherwise, add z*xm*(m*yx+y) / (t*ym+1*((m+1)!)) to lamunq.
  8. Add 1 to n and go to step 4.
  9. Let bound be lam+1/(2k). If lamunq+ϵbound, set s to 0. Otherwise, if lamunq > bound, set s to 2. Otherwise, set s to 1.
  10. Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), go to step 3. Otherwise, return a number that is 0 if s is 0, 1 if s is 2, or an unbiased random bit (either 0 or 1 with equal probability) otherwise.

ln(1+y/z)

See also the algorithm given earlier for ln(1+λ). In this algorithm, y/z is a rational number in the interval [0, 1]. (Thus, the special case ln(2) results when y/z = 1/1.)

  1. If y/z is 0, return 0.
  2. Do the following process repeatedly, until this algorithm returns a value:
    1. Generate an unbiased random bit. If that bit is 1 (which happens with probability 1/2), return a number that is 1 with probability y/z and 0 otherwise.
    2. Generate a uniform(0, 1) random variate u, if u wasn't generated yet.
    3. Sample from the number u, then generate a number that is 1 with probability y/z and 0 otherwise. If the call returns 1 and the number generated is 1, return 0.

Requests and Open Questions

See my page "Open Questions on the Bernoulli Factory Problem" for open questions, answers to which will greatly improve my articles on Bernoulli factories. These questions include:

Other questions:

Correctness and Performance Charts

Charts showing the correctness and performance of some of these algorithms are found in a separate page.

Acknowledgments

I acknowledge Luis Mendo, who responded to one of my open questions, as well as C. Karney.

Notes

[^1]: Flajolet, P., Pelletier, M., Soria, M., "On Buffon machines and numbers", arXiv:0906.5560 [math.PR], 2010.

[^2]: Keane, M. S., and O'Brien, G. L., "A Bernoulli factory", ACM Transactions on Modeling and Computer Simulation 4(2), 1994.

[^3]: There is an analogue to the Bernoulli factory problem called the quantum Bernoulli factory, with the same goal of simulating functions of unknown probabilities, but this time with algorithms that employ quantum-mechanical operations (unlike classical algorithms that employ no such operations). However, quantum-mechanical programming is far from being accessible to most programmers at the same level as classical programming, and will likely remain so for the foreseeable future. For this reason, the quantum Bernoulli factory is outside the scope of this document, but it should be noted that more factory functions can be "constructed" using quantum-mechanical operations than by classical algorithms. For example, a factory function defined in [0, 1] has to meet the requirements proved by Keane and O'Brien except it can touch 0 and/or 1 at a finite number of points in the domain (Dale, H., Jennings, D. and Rudolph, T., 2015, "Provable quantum advantage in randomness processing", Nature communications 6(1), pp. 1-4).

[^4]: Huber, M., "Nearly optimal Bernoulli factories for linear functions", arXiv:1308.1562v2 [math.PR], 2014.

[^5]: Yannis Manolopoulos. 2002. "Binomial coefficient computation: recursion or iteration?", SIGCSE Bull. 34, 4 (December 2002), 65–67. DOI: https://doi.org/10.1145/820127.820168.

[^6]: Goyal, V. and Sigman, K., 2012. On simulating a class of Bernstein polynomials. ACM Transactions on Modeling and Computer Simulation (TOMACS), 22(2), pp.1-5.

[^7]: Weikang Qian, Marc D. Riedel, Ivo Rosenberg, "Uniform approximation and Bernstein polynomials with coefficients in the unit interval", European Journal of Combinatorics 32(3), 2011, https://doi.org/10.1016/j.ejc.2010.11.004 http://www.sciencedirect.com/science/article/pii/S0195669810001666

[^8]: Wästlund, J., "Functions arising by coin flipping", 1999.

[^9]: Then j is a binomial random variate expressing the number of successes in n trials that each succeed with probability λ.

[^10]: Qian, W. and Riedel, M.D., 2008, June. The synthesis of robust polynomial arithmetic with stochastic logic. In 2008 45th ACM/IEEE Design Automation Conference (pp. 648-653). IEEE.

[^11]: Thomas, A.C., Blanchet, J., "A Practical Implementation of the Bernoulli Factory", arXiv:1106.2508v3 [stat.AP], 2012.

[^12]: S. Ray, P.S.V. Nataraj, "A Matrix Method for Efficient Computation of Bernstein Coefficients", Reliable Computing 17(1), 2012.

[^13]: And this shows that the polynomial couldn't be simulated if c were allowed to be 1, since the required degree would be infinity; in fact, the polynomial would touch 1 at the point 0.5 in this case, ruling out its simulation by any algorithm (see "About Bernoulli Factories", earlier).

[^14]: Niazadeh, R., Paes Leme, R., Schneider, J., "Combinatorial Bernoulli Factories: Matchings, Flows, and Polytopes", in Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing, pp. 833-846, June 2021; also at https://arxiv.org/abs/2011.03865.pdf.

[^15]: Mossel, Elchanan, and Yuval Peres. New coins from old: computing with unknown bias. Combinatorica, 25(6), pp.707-724, 2005.

[^16]: Nacu, Şerban, and Yuval Peres. "Fast simulation of new coins from old", The Annals of Applied Probability 15, no. 1A (2005): 93-115.

[^17]: Giulio Morina. Krzysztof Łatuszyński. Piotr Nayar. Alex Wendland. "From the Bernoulli factory to a dice enterprise via perfect sampling of Markov chains." Ann. Appl. Probab. 32 (1) 327 - 359, February 2022. https://doi.org/10.1214/21-AAP1679

[^18]: Propp, J.G., Wilson, D.B., "Exact sampling with coupled Markov chains and applications to statistical mechanics", 1996.

[^19]: Mendo, Luis. "An asymptotically optimal Bernoulli factory for certain functions that can be expressed as power series." Stochastic Processes and their Applications 129, no. 11 (2019): 4366-4384.

[^20]: Łatuszyński, K., Kosmidis, I., Papaspiliopoulos, O., Roberts, G.O., "Simulating events of unknown probabilities via reverse time martingales", arXiv:0907.4018v2 [stat.CO], 2009/2011.

[^21]: Flegal, J.M., Herbei, R., "Exact sampling from intractible probability distributions via a Bernoulli factory", Electronic Journal of Statistics 6, 10-37, 2012.

[^22]: Brassard, G., Devroye, L., Gravel, C., "Remote Sampling with Applications to General Entanglement Simulation", Entropy 2019(21)(92), https://doi.org/10.3390/e21010092 .

[^23]: Devroye, L., Non-Uniform Random Variate Generation, 1986.

[^24]: Note that u * BASEk is not just within BASEk of its "true" result, but also not more than that result. Hence pk + 1 <= u rather than pk + 2 <= u.

[^25]: Bill Gosper, "Continued Fraction Arithmetic", 1978.

[^26]: Borwein, J. et al. “Continued Logarithms and Associated Continued Fractions.” Experimental Mathematics 26 (2017): 412 - 429.

[^27]: Penaud, J.G., Roques, O., "Tirage à pile ou face de mots de Fibonacci", Discrete Mathematics 256, 2002.

[^28]: Mendo, L., "Simulating a coin with irrational bias using rational arithmetic", arXiv:2010.14901 [math.PR], 2020/2021.

[^29]: Carvalho, Luiz Max, and Guido A. Moreira. "Adaptive truncation of infinite sums: applications to Statistics", arXiv:2202.06121 (2022).

[^30]: The error term, which follows from the so-called Lagrange remainder for Taylor series, has a numerator of 2 because 2 is higher than the maximum value at the point 1 (in cosh(1)) that f's slope, slope-of-slope, etc. functions can achieve.

[^31]: Kozen, D., "Optimal Coin Flipping", 2014.

[^32]: K. Bringmann, F. Kuhn, et al., “Internal DLA: Efficient Simulation of a Physical Growth Model.” In: Proc. 41st International Colloquium on Automata, Languages, and Programming (ICALP'14), 2014.

[^33]: Dughmi, Shaddin, Jason Hartline, Robert D. Kleinberg, and Rad Niazadeh. "Bernoulli factories and black-box reductions in mechanism design." Journal of the ACM (JACM) 68, no. 2 (2021): 1-30.

[^34]: Huber, M., "Optimal linear Bernoulli factories for small mean problems", arXiv:1507.00843v2 [math.PR], 2016.

[^35]: Schmon, S.M., Doucet, A. and Deligiannidis, G., 2019, April. Bernoulli race particle filters. In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 2350-2358).

[^36]: Agrawal, S., Vats, D., Łatuszyński, K. and Roberts, G.O., 2021. "Optimal Scaling of MCMC Beyond Metropolis", arXiv:2104.02020.

[^37]: However, the number of flips needed by this method will then grow without bound as $\lambda$ approaches 1. Also, this article avoids calling the value X produced this way a "geometric" random variate. Indeed, there is no single way to give the probabilities of a "geometric" random variate; different academic works define the variate differently.

[^38]: Flajolet, Ph., "Analytic models and ambiguity of context-free languages", Theoretical Computer Science 49, pp. 283-309, 1987

[^39]: Here, "choose(X, X/t)" means that out of X letters, X/t of them must be A's, and "(β−1)XX/t" is the number of words that have XX/t letters other than A, given that the remaining letters were A's.

[^40]: In this formula, which is similar to Example 2's, the division by βX*α−X brings W(X) from the interval [0, βg*α] ((X*α)-letter words) to the interval [0, βX] (X-letter words), as required by the main algorithm.

[^41]: Since academic works define the geometric distribution differently, the use of "geometric" here is deliberately kept ambiguous.

[^42]: Peres, Y., "Iterating von Neumann's procedure for extracting random bits", Annals of Statistics 1992,20,1, p. 590-597.

[^43]: "x is even" means that x is an integer and divisible by 2. This is true if x − 2*floor(x/2) equals 0, or if x is an integer and the least significant bit of abs(x) is 0.

[^44]: "x is odd" means that x is an integer and not divisible by 2. This is true if x − 2*floor(x/2) equals 1, or if x is an integer and the least significant bit of abs(x) is 1.

[^45]: Another algorithm for exp(−λ) involves the von Neumann schema, but unfortunately, it converges slowly as λ approaches 1.

[^46]: Gonçalves, F. B., Łatuszyński, K. G., Roberts, G. O. (2017). Exact Monte Carlo likelihood-based inference for jump-diffusion processes.

[^47]: Vats, D., Gonçalves, F. B., Łatuszyński, K. G., Roberts, G. O., "Efficient Bernoulli factory Markov chain Monte Carlo for intractable posteriors", Biometrika 109(2), June 2022 (also in arXiv:2004.07471 [stat.CO]).

[^48]: There are two other algorithms for this function, but they both converge very slowly when λ is very close to 1. One is the general martingale algorithm with $g(\lambda)=\lambda$, $d_0 = 1$, and $a_i=(-1)^i$, due to the function's form as an alternating series. The other is the so-called "even-parity" construction from Flajolet et al. 2010: "(1) Flip the input coin. If it returns 0, return 1. (2) Flip the input coin. If it returns 0, return 0. Otherwise, go to step 1."

[^49]: Peres, N., Lee, A.R. and Keich, U., 2021. Exactly computing the tail of the Poisson-Binomial Distribution. ACM Transactions on Mathematical Software (TOMS), 47(4), pp.1-19.

[^50]: Sadowsky, Bucklew, On large deviations theory and asymptotically efficient Monte Carlo estimation, IEEE Transactions on Information Theory 36 (1990)

[^51]: Lee, A., Doucet, A. and Łatuszyński, K., 2014. "Perfect simulation using atomic regeneration with application to Sequential Monte Carlo", arXiv:1407.5770v1 [stat.CO].

[^52]: Morina, Giulio (2021) Extending the Bernoulli Factory to a dice enterprise. PhD thesis, University of Warwick.

[^53]: Huber, M., "Designing perfect simulation algorithms using local correctness", arXiv:1907.06748v1 [cs.DS], 2019.

[^54]: The even-parity version from Flajolet et al. (2010) could be written as follows. Do the following process repeatedly, until this algorithm returns a value: (1) Generate a uniform(0, 1) random variate u, if u wasn't generated yet; (2) Sample from the number u twice, and flip the input coin twice. If any of these calls or flips returns 0, return 1; (3) Sample from the number u twice, and flip the input coin twice. If any of these calls or flips returns 0, return 0.

[^55]: The even-parity version from Flajolet et al. (2010) could be written as follows. Do the following process repeatedly, until this algorithm returns a value: (1) Flip the input coin. If it returns 0, flip the coin again and return the result; (2) Generate a uniform(0, 1) random variate u, if u wasn't generated yet; (3) Sample from the number u. If the result is 0, flip the input coin and return the result; (4) Flip the input coin. If it returns 0, return 0; (5) Sample from the number u. If the result is 0, return 0.

[^56]: One of the only implementations I could find of this, if not the only, was a Haskell implementation.

[^57]: There is another algorithm for tanh(λ), based on Lambert's continued fraction for tanh(.), but it works only for λ in [0, 1]. The algorithm begins with k equal to 1. Then: (1) If k is 1, generate an unbiased random bit, then if that bit is 1, flip the input coin and return the result; (2) If k is greater than 1, then with probability k/(1+k), flip the input coin twice, and if either or both flips returned 0, return 0, and if both flips returned 1, return a number that is 1 with probability 1/k and 0 otherwise; (3) Do a separate run of the currently running algorithm, but with k = k + 2. If the separate run returns 1, return 0; (4) Go to step 2.

[^58]: Another algorithm for this function uses the general martingale algorithm with $g(\lambda)=\lambda$, $d_0 = 1$ and $a_i=(-1)^{i+1}/i$ (except $a_0 = 0$), but uses more bits on average as λ approaches 1.

[^59]: Canonne, C., Kamath, G., Steinke, T., "The Discrete Gaussian for Differential Privacy", arXiv:2004.00010 [cs.DS], 2020.

[^60]: Forsythe, G.E., "Von Neumann's Comparison Method for Random Sampling from the Normal and Other Distributions", Mathematics of Computation 26(120), October 1972.

[^61]: Sondow, Jonathan. “New Vacca-Type Rational Series for Euler's Constant and Its 'Alternating' Analog ln 4/π.”, 2005.

[^62]: von Neumann, J., "Various techniques used in connection with random digits", 1951.

[^63]: Pae, S., "Random number generation using a biased source", dissertation, University of Illinois at Urbana-Champaign, 2005.

[^64]: Monahan, J.. "Extensions of von Neumann’s method for generating random variables." Mathematics of Computation 33 (1979): 1065-1069.

[^65]: Tsai, Yi-Feng, Farouki, R.T., "Algorithm 812: BPOLY: An Object-Oriented Library of Numerical Algorithms for Polynomials in Bernstein Form", ACM Trans. Math. Softw. 27(2), 2001.

Appendix

 

Using the Input Coin Alone for Randomness

A function f(λ) is strongly simulable (Keane and O'Brien 1994)[^19] if there is a Bernoulli factory algorithm for that function that uses only the input coin as its source of randomness.

If a Bernoulli factory algorithm uses a fair coin, it can often generate flips of the fair coin using the input coin instead, with the help of randomness extraction techniques.

Example: If a Bernoulli factory algorithm would generate an unbiased random bit, instead it could flip the input coin twice until the flip returns 0 then 1 or 1 then 0 this way, then take the result as 0 or 1, respectively (von Neumann 1951)[^62]. But this trick works only if the input coin's probability of heads is neither 0 nor 1.

When Keane and O'Brien (1994)[^19] introduced Bernoulli factories, they showed already that f(λ) is strongly simulable whenever it admits a Bernoulli factory and its domain includes neither 0 nor 1 (so the input coin doesn't show heads every time or tails every time) — just use the von Neumann trick as in the example above. But does f remain strongly simulable if its domain includes 0 and/or 1? That's a complexer question; see the supplemental notes.

The Entropy Bound

There is a lower bound on the average number of coin flips needed to turn a coin with one probability of heads (λ) into a coin with another (τ = f(λ)). It's called the entropy bound (see, for example, (Pae 2005)[^63], (Peres 1992)[^42]) and is calculated as—

For example, if f(λ) is a constant, an algorithm whose only randomness comes from the input coin will require more coin flips to simulate that constant, the more strongly that coin leans towards heads or tails. But this formula works only for such algorithms, even if f isn't a constant.

For certain values of λ, Kozen (2014)[^31] showed a tighter lower bound of this kind, but in general, this bound is not so easy to describe and assumes λ is known. However, if λ is 1/2 (the input coin is unbiased), this bound is simple: at least 2 flips of the input coin are needed on average to simulate a known constant τ, except when τ is a multiple of 1/(2n) for some integer n.

Bernoulli Factories and Unbiased Estimation

If an algorithm—

the algorithm acts as an unbiased estimator of f(λ) that produces estimates in [0, 1] with probability 1 (Łatuszyński et al. 2009/2011)[^20]. (And an estimator like this is possible only if f is a factory function; see Łatuszyński.) Because the algorithm is unbiased, its expected value (or mean or "long-run average") is f(λ). Here's one result of unbiasedness: Take a sample of n independent outputs of the algorithm, sum them, then divide by n. Then with probability 1, this average approaches f(λ) as n gets large. This is the law of large numbers in action.

On the other hand—

is not necessarily an unbiased estimator of f(λ), even if λ′ is an unbiased estimator.

This page focuses on unbiased estimators because "exact sampling" depends on it. See also (Mossel and Peres 2005, section 4)[^15].

Note: Bias and variance are the two sources of error in a randomized estimation algorithm. An unbiased estimator has no bias, but is not without error. In the case at hand here, the variance of a Bernoulli factory for f(λ) equals f(λ) * (1−f(λ)) and can go as high as 1/4. ("Variance reduction" methods are outside the scope of this document.) An estimation algorithm's mean squared error equals variance plus square of bias.

Proof of the General Martingale Algorithm

The proof is similar to the proof for certain alternating series with only nonzero coefficients, given in Łatuszyński et al. (2019/2011)[^3], section 3.1. Suppose we repeatedly flip a coin that shows heads with probability $g(\lambda)$ and we get the following results: $X_1, X_2, ...$, where each result is either 1 if the coin shows heads or 0 otherwise. Then define two sequences U and L as follows:

Then it's clear that with probability 1, for every $n\ge 1$—

Moreover, if there are infinitely many nonzero coefficients, the U and L sequences have expected values ("long-run averages") converging to $f(\lambda)$ with probability 1; otherwise $f(\lambda)$ is a polynomial in $g(\lambda)$, and $U_n$ and $L_n$ have expected values that approach $f(\lambda)$ as $n$ gets large. These conditions are required for the paper's Algorithm 3 (and thus the algorithm given above) to be valid.

Correctness Proof for the Continued Logarithm Simulation Algorithm

Theorem. If the algorithm given in "Continued Logarithms" terminates with probability 1, it returns 1 with probability exactly equal to the number represented by the continued logarithm c, and 0 otherwise.

Proof. This proof of correctness takes advantage of Huber's "fundamental theorem of perfect simulation" (Huber 2019)[^53]. Using Huber's theorem requires proving two things:

Since both conditions of Huber's theorem are satisfied, this completes the proof. □

Correctness Proof for Continued Fraction Simulation Algorithm 3

Theorem. Suppose a generalized continued fraction's partial numerators are b[i] and all greater than 0, and its partial denominators are a[i] and all 1 or greater, and suppose further that each b[i]/a[i] is 1 or less. Then the algorithm given as Algorithm 3 in "Continued Fractions" returns 1 with probability exactly equal to the number represented by that continued fraction, and 0 otherwise.

Proof. We use Huber's "fundamental theorem of perfect simulation" again in the proof of correctness.

Probabilities Arising from Certain Permutations

Certain interesting probability functions can arise from permutations.

Inspired by the von Neumann schema, we can describe the following algorithm:

Let a permutation class (defined in "Flajolet's Probability Simulation Schemes") and two distributions D and E, which are both continuous with probability density functions, be given. Consider the following algorithm: Generate a sequence of independent random variates (where the first is distributed as D and the rest as E) until the sequence no longer follows the permutation class, then return n, which is how many numbers were generated this way minus 1.

Then the algorithm's behavior is given in the tables below.

Permutation Class Distributions D and E The algorithm returns n with this probability: The probability that n is ...
Numbers sorted in descending order Arbitrary; D = E n / ((n + 1)!). Odd is 1−exp(−1); even is exp(−1). See note 3.
Numbers sorted in descending order Each arbitrary (∫(−∞,∞) DPDF(z) * ((ECDF(z))n−1/((n−1)!) − (ECDF(z))n/(n!)) dz), for every n > 0 (see also proof of Theorem 2.1 of (Devroye 1986, Chapter IV)[^23]. DPDF and ECDF are defined later. Odd is denominator of formula 1 below.
Alternating numbers Arbitrary; D = E (an * (n + 1) − an + 1) / (n + 1)!, where ai is the integer at position i (starting at 0) of the sequence A000111 in the On-Line Encyclopedia of Integer Sequences. Odd is 1−cos(1)/(sin(1)+1); even is cos(1)/(sin(1)+1). See note 3.
Any Arbitrary; D = E (∫[0, 1] 1 * (zn−1*V(n)/((n−1)!) − zn*V(n+1)/(n!)) dz), for every n > 0. V(n) is the number of permutations of size n that belong in the permutation class. For this algorithm, V(n) must be in the interval (0, n!]; this algorithm won't work, for example, if there are 0 permutations of odd size. Odd is 1 − 1 / EGF(1); even is 1/EGF(1).
Less than k is (V(0) − V(k)/(k!)) / V(0). See note 3.
Permutation Class Distributions D and E The probability that the first number in the sequence is x or less given that n is ...
Numbers sorted in descending order Each arbitrary Odd is ψ(x) = (∫(−∞, x) exp(−ECDF(z)) * DPDF(z) dz) / (∫(−∞, ∞) exp(−ECDF(z)) * DPDF(z) dz) (Formula 1; see Theorem 2.1(iii) of (Devroye 1986, Chapter IV)[^23]; see also Forsythe 1972[^60]). Here, DPDF is the probability density function (PDF) of D, and ECDF is the cumulative distribution function for E.
If x is uniform in (0, 1), this probability becomes ∫[0, 1] ψ(z) dz.
Numbers sorted in descending order Each arbitrary Even is (∫(−∞, x) (1 − exp(−ECDF(z))) * DPDF(z) dz) / (∫(−∞, ∞) (1 − exp(−ECDF(z))) * DPDF(z) dz) (Formula 2; see also Monahan 1979[^64]). DPDF and ECDF are as above.
Numbers sorted in descending order Both uniform in (0,1) Odd is ((1−exp(−x)))/(1−exp(1)). Therefore, the first number in the sequence is distributed as exponential with rate 1 and "cut off" to the interval [0, 1] (von Neumann 1951)[^62].
Numbers sorted in descending order D is uniform in (0,1); E is max. of two uniform variates in (0,1). Odd is erf(x)/erf(1) (uses Formula 1, where DPDF(z) = 1 and ECDF(z) = z2 for z in [0, 1]; see also erf(x)/erf(1)).

Notes:

  1. All the functions possible for formulas 1 and 2 are nondecreasing functions. Both formulas express what are called cumulative distribution functions, namely FD(x given that n is odd) or FD(x given that n is even), respectively.
  2. EGF(z) is the exponential generating function (EGF) for the kind of permutation involved in the algorithm. For example, the class of alternating permutations (permutations whose numbers alternate between low and high, that is, X1 > X2 < X3 > ...) uses the EGF tan(λ)+1/cos(λ). Other examples of EGFs were given in the section on the von Neumann schema.
  3. The results that point to this note have the special case that both D and E are uniform in (0, 1). Indeed, if each variate x in the sequence is transformed with CDF(x), where CDF is D's cumulative distribution function, then with probability 1, the variates become uniform in (0, 1), with the same numerical order as before. See also this Stack Exchange question.

Sketch of Derivation of the Algorithm for 1 / π

The Flajolet paper presented an algorithm to simulate 1 / π but provided no derivation. Here is a sketch of how this algorithm works.

The algorithm is an application of the convex combination technique. Namely, 1 / π can be seen as a convex combination of two components:

Notes:

  1. 9 * (n + 1) / (22 * n + 4) is the probability that the sum of two independent random variates equals n, where each of the two variates expresses the number of failures before the first success and the chance of a success is 1−1/4.
  2. pm * (1 − p)n * choose(n + m − 1, m − 1) is the probability that the sum of m independent random variates equals n (a negative binomial distribution), where each of the m variates expresses the number of failures before the first success and the chance of a success is p.
  3. p * f(z − 1) + (1 − p) * f(z) is the probability that the sum of two independent random variates — a Bernoulli variate with mean p as well as an integer that equals x with probability f(x) — equals z.

Preparing Rational Functions

This section describes how to turn a single-variable rational function (ratio of polynomials) into an array of polynomials needed to apply the "Dice Enterprise" special case described in "Certain Rational Functions". In short, the steps to do so can be described as separating, homogenizing, and augmenting.

Separating. If a rational function's numerator (D) and denominator (E) are written—

then the function can be separated into two polynomials that sum to the denominator. (Here, i+j is the term's degree, and the polynomial's degree is the highest degree among its terms.) To do this separation, subtract the numerator from the denominator to get a new polynomial (G) such that G = ED (or D + G = E). (Then D and G are the two polynomials that will be used.) Similarly, if we have multiple rational functions with a common denominator, namely (D1/E), ..., (DN/E), where D1, ..., DN and E are written in form 1, then they can be separated into N + 1 polynomials by subtracting the numerators from the denominator, so that G = ED1 − ... − DN. (Then D1, ..., DN and G are the polynomials that will be used.) To use the polynomials in the algorithm, however, they need to be homogenized, then augmented, as described next.

Example: We have the rational function (4*λ1*(1−λ)2) / (7 − 5*λ1*(1−λ)2). Subtracting the numerator from the denominator leads to: 7 − 1*λ1*(1−λ)2.

Homogenizing. The next step is to homogenize the polynomials so they have the same degree and a particular form. For this step, choose n to be an integer no less than the highest degree among the polynomials.

Suppose a polynomial—

Then the polynomial can be turned into a homogeneous polynomial of degree n (all its terms have degree n) as follows.

If the polynomial is written in so-called "power form" as c[0] + c[1]*λ + c[2]*λ2 + ... + c[n]*λn, then the method is instead as follows:

Example: We have the following polynomial: 3*λ2 + 10*λ1*(1−λ)2. This is a degree-3 polynomial, and we seek to turn it into a degree-5 homogeneous polynomial. The result becomes the sum of the terms—

resulting in the coefficients (0, 10, 23, 19, 9, 3) for the new homogeneous polynomial.

Augmenting. If we have an array of homogeneous single-variable polynomials of the same degree, they are ready for use in the Dice Enterprise special case if—

If those conditions are not met, then each polynomial can be augmented as often as necessary to meet the conditions (Morina et al., 2022)[^17]. For polynomials of the kind relevant here, augmenting a polynomial amounts to degree elevation similar to that of polynomials in Bernstein form (see also Tsai and Farouki 2001[^65]). It is implemented as follows:

According to the Morina paper, it's enough to do n augmentations on each polynomial for the whole array to meet the conditions above (although fewer than n will often suffice).

Note: For best results, the input polynomials' coefficients should be rational numbers. If they are not, then special methods are needed to ensure exact results, such as interval arithmetic that calculates lower and upper bounds.

License

Any copyright to this page is released to the Public Domain. In case this is not possible, this page is also licensed under Creative Commons Zero.