More Random Sampling Methods

Peter Occil


Specific Distributions

Requires random real numbers. This section shows algorithms to sample several popular non-uniform distributions. The algorithms are exact unless otherwise noted, and applications should choose algorithms with either no error (including rounding error) or a user-settable error bound. See the appendix for more information.

Normal (Gaussian) Distribution

The normal distribution (also called the Gaussian distribution) takes the following two parameters:

There are a number of methods for sampling the normal distribution. An application can combine some or all of these.

  1. The ratio-of-uniforms method (given as NormalRatioOfUniforms below).
  2. In the Box–Muller transformation, mu + radius * cos(angle) and mu + radius * sin(angle), where angle = RNDRANGEMaxExc(0, 2 * pi) and radius = sqrt(Expo(0.5)) * sigma, are two independent values sampled from the normal distribution. The polar method (given as NormalPolar below) likewise produces two independent values sampled from that distribution at a time.
  3. Karney's algorithm to sample from the normal distribution, in a manner that minimizes approximation error and without using floating-point numbers (Karney 2014)(1).

For surveys of Gaussian samplers, see (Thomas et al. 2007)(2), and (Malik and Hemani 2016)(3).

METHOD NormalRatioOfUniforms(mu, sigma)
    while true
        if b*b <= -a * a * 4 * ln(a)
          return (RNDINT(1) * 2 - 1) *
            (b * sigma / a) + mu

METHOD NormalPolar(mu, sigma)
  while true
    a = RNDRANGEMinExc(0,1)
    b = RNDRANGEMinExc(0,1)
    if RNDINT(1) == 0: a = 0 - a
    if RNDINT(1) == 0: b = 0 - b
    c = a * a + b * b
    if c != 0 and c <= 1
       c = sqrt(-ln(c) * 2 / c)
       return [a * sigma * c + mu, b * sigma * c + mu]


  1. The standard normal distribution is implemented as Normal(0, 1).
  2. Methods implementing a variant of the normal distribution, the discrete Gaussian distribution, generate integers that closely follow the normal distribution. Examples include the one in (Karney 2014)(1), an improved version in (Du et al. 2021)(4), as well as so-called "constant-time" methods such as (Micciancio and Walter 2017)(5) that are used above all in lattice-based cryptography.
  3. The following are some approximations to the normal distribution that papers have suggested:
    • The sum of twelve RNDRANGEMaxExc(0, sigma) numbers, subtracted by 6 * sigma, to generate an approximate normal variate with mean 0 and standard deviation sigma. (Kabal 2000/2019)(6) "warps" this sum in the following way (before adding the mean mu) to approximate the normal distribution better: ssq = sum * sum; sum = ((((0.0000001141*ssq - 0.0000005102) * ssq + 0.00007474) * ssq + 0.0039439) * ssq + 0.98746) * sum. See also "Irwin–Hall distribution", namely the sum of n many RNDRANGE(0, 1) numbers, on Wikipedia. D. Thomas (2014)(7), describes a more general approximation called CLTk, which combines k numbers in [0, 1] sampled from the uniform distribution as follows: RNDRANGE(0, 1) - RNDRANGE(0, 1) + RNDRANGE(0, 1) - ....
    • Numerical inversions of the normal distribution's cumulative distribution function (CDF), including those by Wichura, by Acklam, and by Luu (Luu 2016)(8). See also "A literate program to compute the inverse of the normal CDF". Notice that the normal distribution's inverse CDF has no closed form.
  4. A pair of q-Gaussian random variates with parameter q less than 3 can be generated using the Box–Muller transformation, except radius is radius=sqrt(-2*(pow(u,1-qp)-1)/(1-qp)) (where qp=(1+q)/(3-q) and u=RNDRANGE(0, 1)), and the two variates are not independent of each other (Thistleton et al. 2007)(9).
  5. A well-known result says that adding n many Normal(0, 1) variates, and dividing by sqrt(n), results in a new Normal(0, 1) variate.

Gamma Distribution

The following method samples a number from a gamma distribution and is based on Marsaglia and Tsang's method from 2000(10) and (Liu et al. 2015)(11). Usually, the number expresses either—

Here, meanLifetime must be an integer or noninteger greater than 0, and scale is a scaling parameter that is greater than 0, but usually 1 (the random gamma number is multiplied by scale).

METHOD GammaDist(meanLifetime, scale)
    // Needs to be greater than 0
    if meanLifetime <= 0 or scale <= 0: return error
    // Exponential distribution special case if
    // `meanLifetime` is 1 (see also (Devroye 1986), p. 405)
    if meanLifetime == 1: return Expo(1.0 / scale)
    if meanLifetime < 0.3 // Liu, Martin, Syring 2015
       lamda = (1.0/meanLifetime) - 1
       w = meanLifetime / (1-meanLifetime) * exp(1)
       r = 1.0/(1+w)
       while true
            z = 0
            x = RNDRANGE(0, 1)
            if x <= r: z = -ln(x/r)
            else: z = -Expo(lamda)
            ret = exp(-z/meanLifetime)
            eta = 0
            if z>=0: eta=exp(-z)
            else: eta=w*lamda*exp(lamda*z)
            if RNDRANGE(0, eta) < exp(-ret-z): return ret * scale
    d = meanLifetime
    v = 0
    if meanLifetime < 1: d = d + 1
    d = d - (1.0 / 3) // NOTE: 1.0 / 3 must be a fractional number
    c = 1.0 / sqrt(9 * d)
    while true
        x = 0
        while true
           x = Normal(0, 1)
           v = c * x + 1;
           v = v * v * v
           if v > 0: break
        u = RNDRANGEMinExc(0,1)
        x2 = x * x
        if u < 1 - (0.0331 * x2 * x2): break
        if ln(u) < (0.5 * x2) + (d * (1 - v + ln(v))): break
    ret = d * v
    if meanLifetime < 1
       ret = ret * pow(RNDRANGE(0, 1), 1.0 / meanLifetime)
    return ret * scale

Note: The following is a useful identity for the gamma distribution: GammaDist(a) = BetaDist(a, b - a) * GammaDist(b) (Stuart 1962)(12).

Beta Distribution

The beta distribution is a bounded-domain probability distribution; its two parameters, a and b, are both greater than 0 and describe the distribution's shape. Depending on a and b, the shape can be a smooth peak or a smooth valley.

The following method samples a number from a beta distribution, in the interval [0, 1).

METHOD BetaDist(a, b)
  if b==1 and a==1: return RNDRANGE(0, 1)
  // Min-of-uniform
  if a==1: return 1.0-pow(RNDRANGE(0, 1),1.0/b)
  // Max-of-uniform.  Use only if a is small to
  // avoid accuracy problems, as pointed out
  // by Devroye 1986, p. 675.
  if b==1 and a < 10: return pow(RNDRANGE(0, 1),1.0/a)
  return x/(x+GammaDist(b,1))

I give an error-bounded sampler for the beta distribution (when a and b are both 1 or greater) in a separate page.

Noncentral Hypergeometric Distributions

The following variants of the hypergeometric distribution are described in detail by Agner Fog in "Biased Urn Theory".

Let there be m balls that each have one of two or more colors. For each color, assign each ball of that color the same weight (0 or greater). Then:

  1. Wallenius's hypergeometric distribution: Choose one ball not yet chosen, with probability equal to its weight divided by the sum of weights of balls not yet chosen. Repeat until exactly n items are chosen this way. Then for each color, count the number of items of that color chosen this way.
  2. Fisher's hypergeometric distribution: For each ball, choose it with probability equal to its weight divided by the sum of weights of all balls. (Thus, each ball is independently chosen or not chosen depending on its weight.) If exactly n items were chosen this way, stop. Otherwise, start over. Then for each color, count the number of items of that color chosen this way.

For both distributions, if there are two colors, there are four parameters: m, ones, n, weight, such that—

von Mises Distribution

The von Mises distribution describes a distribution of circular angles and uses two parameters: mean is the mean angle and kappa is a shape parameter. The distribution is uniform at kappa = 0 and approaches a normal distribution with increasing kappa.

The algorithm below samples a number from the von Mises distribution, and is based on the Best–Fisher algorithm from 1979 (as described in (Devroye 1986)(13) with errata incorporated).

METHOD VonMises(mean, kappa)
    if kappa < 0: return error
    if kappa == 0
        return RNDRANGEMinMaxExc(mean-pi, mean+pi)
    r = 1.0 + sqrt(4 * kappa * kappa + 1)
    rho = (r - sqrt(2 * r)) / (kappa * 2)
    s = (1 + rho * rho) / (2 * rho)
    while true
        u = RNDRANGEMaxExc(-pi, pi)
        v = RNDRANGEMinMaxExc(0, 1)
        z = cos(u)
        w = (1 + s*z) / (s + z)
        y = kappa * (s - w)
        if y*(2 - y) - v >=0 or ln(y / v) + 1 - y >= 0
           if angle<-1: angle=-1
           if angle>1: angle=1
           // NOTE: Inverse cosine replaced here
           // with `atan2` equivalent
           angle = atan2(sqrt(1-w*w),w)
           if u < 0: angle = -angle
           return mean + angle

Stable Distribution

As more and more numbers, sampled independently at random in the same way, are added together, their distribution tends to a stable distribution, which resembles a curve with a single peak, but with generally "fatter" tails than the normal distribution. (Here, the stable distribution means the "alpha-stable distribution".) The pseudocode below uses the Chambers–Mallows–Stuck algorithm. The Stable method, implemented below, takes two parameters:


METHOD Stable(alpha, beta)
    if alpha <=0 or alpha > 2: return error
    if beta < -1 or beta > 1: return error
    halfpi = pi * 0.5
    unif=RNDRANGEMinMaxExc(-halfpi, halfpi)
    if alpha == 1
       if beta == 0: return s/c
       return 2.0*((unif*beta+halfpi)*s/c -
         beta * ln(halfpi*expo*c/(unif*beta+halfpi)))/pi
       ug=unif+atan2(-z, 1)/alpha
       cpow=pow(c, -1.0 / alpha)
       return pow(1.0+z*z, 1.0 / (2*alpha))*
          pow(cos(unif-alpha*ug)/expo, (1.0 - alpha) / alpha)

Methods implementing the strictly geometric stable and general geometric stable distributions are shown below (Kozubowski 2000)(14). Here, alpha is in (0, 2], lamda is greater than 0, and tau's absolute value is not more than min(1, 2/alpha − 1). The result of GeometricStable is a symmetric Linnik distribution if tau = 0, or a Mittag-Leffler distribution if tau = 1 and alpha < 1.

METHOD GeometricStable(alpha, lamda, tau)
   rho = alpha*(1-tau)/2
   sign = -1
   if tau==1 or RNDINT(1)==0 or RNDRANGE(0, 1) < tau
       rho = alpha*(1+tau)/2
       sign = 1
   w = 1
   if rho != 1
      rho = rho * pi
      cotparam = RNDRANGE(0, rho)
      w = sin(rho)*cos(cotparam)/sin(cotparam)-cos(rho)
   return Expo(1) * sign * pow(lamda*w, 1.0/alpha)

METHOD GeneralGeoStable(alpha, beta, mu, sigma)
   z = Expo(1)
   if alpha == 1: return mu*z+Stable(alpha, beta)*sigma*z+
   else: return mu*z+
          Stable(alpha, beta)*sigma*pow(z, 1.0/alpha)

Multivariate Normal (Multinormal) Distribution

The following pseudocode calculates a random vector (list of numbers) that follows a multivariate normal (multinormal) distribution. The method MultivariateNormal takes the following parameters:


METHOD Decompose(matrix)
  numrows = size(matrix)
  if size(matrix[0])!=numrows: return error
  // Does a Cholesky decomposition of a matrix
  // assuming it's positive definite and invertible
  for i in 0...numrows
    submat = NewList()
    for j in 0...numrows: AddItem(submat, 0)
    AddItem(ret, submat)
  s1 = sqrt(matrix[0][0])
  if s1==0: return ret // For robustness
  for i in 0...numrows
  for i in 0...numrows
    for j in 0...i: msum = msum + ret[j][i]*ret[j][i]
    if sq<0: sq=0 // For robustness
  for j in 0...numrows
    for i in (j + 1)...numrows
      // For robustness
      if ret[j][j]==0: ret[j][i]=0
      if ret[j][j]!=0
        for k in 0...j: msum = msum + ret[k][i]*ret[k][j]
  return ret

METHOD MultivariateNormal(mu, cov)
  if mu != nothing
    mulen = size(mu)
    if mulen!=size(cov): return error
    if mulen!=size(cov[0]): return error
  // NOTE: If multiple random points will
  // be generated using the same covariance
  // matrix, an implementation can consider
  // precalculating the decomposed matrix
  // in advance rather than calculating it here.
  for j in 0...mulen: AddItem(vars, Normal(0, 1))
  while i<mulen
    msum = 0
    if mu == nothing: msum=mu[i]
    for j in 0...mulen: msum=msum+vars[j]*cho[j][i]
    AddItem(ret, msum)
  return ret

Note: The Python sample code contains a variant of this method for generating multiple random vectors in one call.


  1. A vector that follows a binormal distribution (two-variable multinormal distribution) is a vector of two numbers from the normal distribution, and can be sampled using the following idiom: MultivariateNormal([mu1, mu2], [[s1*s1, s1*s2*rho], [rho*s1*s2, s2*s2]]), where mu1 and mu2 are the means of the vector's two components, s1 and s2 are their standard deviations, and rho is a correlation coefficient greater than -1 and less than 1 (0 means no correlation).
  2. Log-multinormal distribution: Generate a multinormal random vector, then apply exp(n) to each component n.
  3. A Beckmann distribution: Generate a random binormal vector vec, then apply Norm(vec) to that vector.
  4. A Rice (Rician) distribution is a Beckmann distribution in which the binormal random pair is generated with m1 = m2 = a / sqrt(2), rho = 0, and s1 = s2 = b, where a and b are the parameters to the Rice distribution.
  5. Rice–Norton distribution: Generate vec = MultivariateNormal([v,v,v],[[w,0,0],[0,w,0],[0,0,w]]) (where v = a/sqrt(m*2), w = b*b/m, and a, b, and m are the parameters to the Rice–Norton distribution), then apply Norm(vec) to that vector.
  6. A standard complex normal distribution is a binormal distribution in which the binormal random pair is generated with s1 = s2 = sqrt(0.5) and mu1 = mu2 = 0 and treated as the real and imaginary parts of a complex number.
  7. Multivariate Linnik distribution: Generate a multinormal random vector, then multiply each component by GeometricStable(alpha/2.0, 1, 1), where alpha is a parameter in (0, 2] (Kozubowski 2000)(14).

Gaussian and Other Copulas

A copula is a way to describe the dependence between randomly sampled numbers.

One example is a Gaussian copula; this copula is sampled by sampling from a multinormal distribution, then converting the resulting numbers to dependent uniform random values. In the following pseudocode, which implements a Gaussian copula:


METHOD GaussianCopula(covar)
   mvn=MultivariateNormal(nothing, covar)
   for i in 0...size(covar)
      // Apply the normal distribution's CDF
      // to get uniform numbers
      mvn[i] = (erf(mvn[i]/(sqrt(2)*sqrt(covar[i][i])))+1)*0.5
   return mvn

Each of the resulting uniform random values will be in the interval [0, 1], and each one can be further transformed to any other probability distribution (which is called a marginal distribution or marginal here) by taking the quantile of that uniform number for that distribution (see "Inverse Transform Sampling", and see also (Cario and Nelson 1997)(15).)

Note: The Gaussian copula is also known as the normal-to-anything method.


  1. To generate two correlated uniform random values with a Gaussian copula, generate GaussianCopula([[1, rho], [rho, 1]]), where rho is the Pearson correlation coefficient, in the interval [-1, 1]. (Other correlation coefficients besides rho exist. For example, for a two-variable Gaussian copula, the Spearman correlation coefficient srho can be converted to rho by rho = sin(srho * pi / 6) * 2. Other correlation coefficients, and other measures of dependence between randomly sampled numbers, are not further discussed in this document.)
  2. The following example generates a two-dimensional random vector that follows a Gaussian copula with exponential marginals (rho is the Pearson correlation coefficient, and rate1 and rate2 are the rates of the two exponential marginals).

    METHOD CorrelatedExpo(rho, rate1, rate2)
       copula = GaussianCopula([[1, rho], [rho, 1]])
       // Transform to exponentials using that
       // distribution's quantile function
       return [-log1p(-copula[0]) / rate1,
         -log1p(-copula[1]) / rate2]
  3. The T–Poisson hierarchy (Knudson et al. 2021)(16) is a way to generate N-dimensional Poisson-distributed random vectors via copulas. Each of the N dimensions is associated with a parameter lamda and a marginal that must be a continuous non-negative probability distribution (one that takes on any of an uncountable number of non-negative values, such as any number 0 or greater). To sample from the T–Poisson hierarchy—

    1. sample an N-dimensional random vector via a copula (such as GaussianCopula), producing an N-dimensional vector of correlated uniform numbers; then
    2. for each component in the vector, take that component's quantile for the corresponding marginal; then
    3. for each component in the vector, replace it with Poisson(lamda * c), where c is that component and lamda is the lamda parameter for the corresponding dimension.

    The following example implements the T-Poisson hierarchy using a Gaussian copula and exponential marginals.

    METHOD PoissonH(rho, rate1, rate2, lambda1, lambda2)
       vec = CorrelatedExpo(rho, rate1, rate2)
       return [Poisson(lambda1*vec[0]),Poisson(lambda2*vec[1])]

Other kinds of copulas describe different kinds of dependence between randomly sampled numbers. Examples of other copulas are—



Implementation of erf

The pseudocode below shows an approximate implementation of the error function erf, in case the programming language used doesn't include a built-in version of erf (such as JavaScript at the time of this writing). In the pseudocode, EPSILON is a very small number to end the iterative calculation.

METHOD erf(v)
    if v==0: return 0
    if v<0: return -erf(-v)
    if v==infinity: return 1
    // NOTE: For Java `double`, the following
    // line can be added:
    // if v>=6: return 1
    while i < 100
        // NOTE: EPSILON can be pow(10,14),
        // for example.
        if abs(r)<EPSILON: break
        if i==1: zval=zp
        else: zval = zval*zp/i
        i = i + 1
    return ret*2/sqrt(pi)

Exact, Error-Bounded, and Approximate Algorithms

There are three kinds of randomization algorithms:

  1. An exact algorithm is an algorithm that samples from the exact distribution requested, assuming that computers—

    • can store and operate on real numbers (which have unlimited precision), and
    • can generate independent uniform random real numbers

    (Devroye 1986, p. 1-2)(13). However, an exact algorithm implemented on real-life computers can incur error due to the use of fixed precision, such as rounding and cancellations, especially when floating-point numbers are involved. An exact algorithm can achieve a guaranteed bound on accuracy (and thus be an error-bounded algorithm) using either arbitrary-precision or interval arithmetic (see also Devroye 1986, p. 2)(13). All methods given on this page are exact unless otherwise noted. Note that the RNDRANGE method is exact in theory, but has no required implementation.

  2. An error-bounded algorithm is a sampling algorithm with the following requirements:

    • If the ideal distribution is discrete (takes on a countable number of values), the algorithm samples exactly from that distribution.
    • If the ideal distribution is continuous, the algorithm samples from a distribution that is close to the ideal within a user-specified error tolerance (see below for details). The algorithm can instead sample a number from the distribution only partially, as long as the fully sampled number can be made close to the ideal within any error tolerance desired.
    • In sampling from a distribution, the algorithm incurs no approximation error not already present in the inputs (except errors needed to round the final result to the user-specified error tolerance).

    Many error-bounded algorithms use random bits as their only source of randomness. An application should use error-bounded algorithms whenever possible.

  3. An inexact, approximate, or biased algorithm is neither exact nor error-bounded; it uses "a mathematical approximation of sorts" to sample from a distribution that is close to the desired distribution (Devroye 1986, p. 2)(13). An application should use this kind of algorithm only if it's willing to trade accuracy for speed.

Most algorithms on this page, though, are not error-bounded, but even so, they may still be useful to an application willing to trade accuracy for speed.

There are many ways to describe closeness between two distributions. One suggestion by Devroye and Gravel (2020)(18) is Wasserstein distance (or "earth-mover distance"). Here, an algorithm has accuracy ε (the user-specified error tolerance) if it samples from a distribution that is close to the ideal distribution by a Wasserstein distance of not more than ε.


  1. Sampling from the exponential distribution via -ln(RNDRANGE(0, 1)) is an exact algorithm (in theory), but not an error-bounded one for common floating-point number formats. The same is true of the Box–Muller transformation.
  2. Sampling from the exponential distribution using the ExpoExact method from the section "Exponential Distribution" is an error-bounded algorithm. Karney's algorithm for the normal distribution (Karney 2014)(1) is also error-bounded because it returns a result that can be made to come close to the normal distribution within any error tolerance desired simply by appending more random digits to the end. See also (Oberhoff 2018)(19).
  3. Examples of approximate algorithms include sampling from a Gaussian-like distribution via a sum of RNDRANGE(0, 1), or most cases of modulo reduction to produce uniform-like integers at random (see notes in the section "RNDINT").


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