The Sampling Problem

Peter Occil

This page is about a mathematical problem of sampling a probability distribution with unknown parameters. This problem can be described as sampling from a new distribution using an endless stream of random variates from an incompletely known distribution.

Suppose there is an endless stream of numbers, each generated at random and independently from each other, and as many numbers can be sampled from the stream as desired. Let $(X_0, X_1, X_2, X_3, …)$ be that endless stream, and call the numbers input values.

Let InDist be the probability distribution of these input values, and let $\lambda$ be an unknown parameter that determines the distribution InDist, such as its expected value (or mean or “long-run average”). Suppose the problem is to produce a random variate with a distribution OutDist that depends on the unknown parameter $\lambda$. Then, of the algorithms in the section “Sampling Distributions Using Incomplete Information”:

• In Algorithm 1 (Jacob and Thiery 2015)¹, InDist is arbitrary but must have a known minimum and maximum, $\lambda$ is the expected value of InDist, and OutDist is non-negative and has an expected value of $f(\lambda)$.
• In Algorithm 2 (Duvignau 2015)², InDist is a fair die with an unknown number of faces, $\lambda$ is the number of faces, and OutDist is a specific distribution that depends on the number of faces.
• In Algorithm 3 (Lee et al. 2014)³, InDist is arbitrary, $\lambda$ is the expected value of InDist, and OutDist is non-negative and has an expected value equal to the mean of $f(X)$, where $X$ is an input value taken.
• In Algorithm 4 (Jacob and Thiery 2015)¹, InDist is arbitrary but must have a known minimum, $\lambda$ is the expected value of InDist, and OutDist is non-negative and has an expected value of $f(\lambda)$.
• In Algorithm 5 (Akahira et al. 1992)⁴, InDist is Bernoulli, $\lambda$ is the expected value of InDist, and OutDist has an expected value of $f(\lambda)$.
• In the Bernoulli factory problem (a problem of turning biased coins to biased coins), InDist is Bernoulli, $\lambda$ is the expected value of InDist, and OutDist is Bernoulli with an expected value of $f(\lambda)$.

In all cases given earlier, each input value is independent of everything else.

There are numerous other cases of interest that are not covered in the algorithms above. An example is the case of Algorithm 5 except InDist is any discrete distribution, not just Bernoulli. ⁵ An interesting topic is to answer the following: In which cases (and for which functions $f$) can the problem be solved…

• …when the number of input values taken is random (and may depend on already taken inputs), but is finite with probability 1 (a sequential unbiased estimator)?⁶
• …when only a fixed number $n$ of input values can be taken (a fixed-size unbiased estimator)?
• …using an algorithm that produces outputs whose expected value approaches $f(\lambda)$ as more input values are taken (an asymptotically unbiased estimator)?

The answers to these questions will depend on—

• the allowed distributions for InDist,
• the allowed distributions for OutDist,
• which parameter $\lambda$ is unknown,
• whether the inputs are independent, and
• whether outside randomness is allowed (that is, whether the estimator is randomized).

An additional question is to find lower bounds on the input/output ratio that an algorithm can achieve as the number of inputs taken increases (e.g., Nacu and Peres (2005, Question 2)⁷).

My interest on the problem is in the existence and construction of simple-to-implement algorithms that solve the sampling problem given here. In addition, the cases that most interest me are when—

• $\lambda$ is InDist’s expected value, and
• OutDist has an expected value of $f(\mathbb{E}[X])$ or $\mathbb{E}[f(X)]$, where $X$ is an input value taken,

with or without other conditions.

Results

It should be noted that many special cases of the sampling problem have been studied and resolved in academic papers and books.

The problem here is one of bringing all these results together in one place.

The following are examples of results for this problem. The estimators are allowed to be randomized (to use outside randomness) unless specified otherwise.

• Suppose InDist takes an unknown finite number $n$ of values with unknown probabilities ($n\ge 1$), $\lambda$ is $n$, and OutDist has an expected value of $\lambda$.
  • No sequential non-randomized unbiased estimator exists, even if $n$ is known to have a maximum of 2 or greater (Christman and Nayak 1994)⁸. ⁹
  • Not aware of conditions for sequential randomized unbiased estimators.
  • Not aware of conditions for fixed-size unbiased estimators.
  • Not aware of conditions for asymptotically unbiased estimators.
• Suppose InDist is a fair die with an unknown number of faces (1 or greater), $\lambda$ is the number of faces, and OutDist has an expected value of $f(\lambda)$.
  • If there is no maximum sample size, a sequential unbiased estimator exists for every $f$ (Christman and Nayak 1994)⁸.
  • If $f$ is unbounded (including when $f(\lambda)=\lambda$), there is no fixed-size non-randomized unbiased estimator that is based only on the sample size and the number of unique items sampled (Christman and Nayak 1994)⁸.
  • Not aware of conditions for more general fixed-size unbiased estimators.
  • Not aware of conditions for asymptotically unbiased estimators.
• Suppose InDist takes on numbers from a finite set; $\lambda$ is the expected value of InDist; and OutDist has an expected value of $f(\lambda)$.
  • A fixed-size nonrandomized unbiased estimator exists only if $f$ is a polynomial in homogeneous form of degree $n$ or less, where $n$ is the number of inputs taken (Lehmann (1983, for coin flips)¹⁰, Paninski (2003, proof of Proposition 8, more generally)¹¹).
  • The existence of randomized sequential unbiased estimators is claimed by Singh (1964)¹². But see Akahira et al. (1992)⁴.
  • Not aware of conditions for sequential nonrandomized unbiased estimators.
  • Not aware of conditions for fixed-size randomized unbiased estimators.
  • Not aware of conditions for asymptotically unbiased estimators.
• Suppose InDist has a finite mean, $\lambda$ is the expected value of InDist, and OutDist is nonnegative and has an expected value of $f(\lambda)$.
  • There is no sequential unbiased estimator (and thus no fixed-size unbiased estimator) (Jacob and Thiery 2015)¹.
  • Not aware of conditions for asymptotically unbiased estimators.
• Suppose InDist has a finite mean and is supported on $[a, \infty)$, $\lambda$ is the expected value of InDist, and OutDist is nonnegative and has an expected value of $f(\lambda)$.
  • A sequential unbiased estimator exists only if $f$ is nowhere decreasing (Jacob and Thiery 2015)¹.¹³
  • Not aware of conditions for fixed-size unbiased estimators.
  • Not aware of conditions for asymptotically unbiased estimators.
• Suppose InDist has a finite mean and is supported on $(\infty, a]$, $\lambda$ is the expected value of InDist, and OutDist is nonnegative and has an expected value of $f(\lambda)$.
  • A sequential unbiased estimator exists only if $f$ is nowhere increasing (Jacob and Thiery 2015)¹.
  • Not aware of conditions for fixed-size unbiased estimators.
  • Not aware of conditions for asymptotically unbiased estimators.
• Suppose InDist is Bernoulli (a “biased coin”), $\lambda$ is the expected value of InDist, and OutDist is Bernoulli with an expected value of $f(\lambda)$.
  • Let $D$ be the set of allowed values for $\lambda$. Thus, $D$ is either the closed unit interval or a subset thereof.
  • A sequential unbiased estimator exists if and only if $f$ is everywhere 0, everywhere 1, or continuous and polynomially bounded on $D$ (Keane and O’Brien 1994)¹⁴.
    • The estimator can be nonrandomized whenever $D$ contains neither 0 nor 1 (Keane and O’Brien 1994)¹⁴. See this section for results when $\lambda$ is allowed to be 0 or 1 (the coin can show heads every time or tails every time).
  • A fixed-size unbiased estimator exists if and only if $f$ is writable as a polynomial of degree $n$ with $n+1$ Bernstein coefficients in the closed unit interval, where $n$ is the number of inputs taken (Goyal and Sigman 2012)¹⁵.
  • Perhaps it is true that an asymptotically unbiased estimator exists if and only if there are polynomials $p_1, p_2, …$ that converge pointwise to $f$ on $D$ (that is, for each $\lambda$ in $D$, $p_n(\lambda)$ approaches $f(\lambda)$ as $n$ increases), and the polynomials’ Bernstein coefficients lie in the closed unit interval (see also Singh (1964)¹²).

There are also three other results on the existence of fixed-size and asymptotically unbiased estimators, but they are relatively hard to translate to this problem in a simple way: Liu and Brown (1993)¹⁶, Hirano and Porter (2012)¹⁷, Bickel and Lehmann (1969)¹⁸. Other results include Gajek (1995)¹⁹ (which has a result on building unbiased estimators from asymptotically unbiased ones), Rychlik (1995)²⁰.

In a result closely related to the sampling problem, given a stream of independent random variates each distributed as $\varphi$ with probability $\lambda$ or as $Q$ otherwise (where $\varphi$ and $Q$ are probability distributions, $\varphi$ and $\lambda$ are known, and $Q$ is unknown), there is no way in general to generate a variate distributed as $Q$, even if values from $Q$ and $\varphi$ must come from the same set of numbers ²¹.

Question

For any case of the sampling problem, suppose the number of input values taken is random. If the number of inputs is allowed to depend on previously taken inputs, do more sequential unbiased estimators exist than otherwise?

Notes

License

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