Package org.apache.commons.rng.sampling.distribution

Distribution samplers

This package contains classes for sampling from statistical distributions.

As of version 1.0, the code for specific distributions was adapted from the corresponding classes in the development version of "Commons Math" (in package org.apache.commons.math4.distribution).

When no specific algorithm is provided, one can still sample from any distribution, using the inverse method, as illustrated in:

• InverseTransformDiscreteSampler
• InverseTransformContinuousSampler

Algorithms are described in e.g. Luc Devroye (1986), chapter 9 and chapter 10. This paper discusses Gaussian generators.

Interface Summary

Interface	Description
ContinuousInverseCumulativeProbabilityFunction	Interface for a continuous distribution that can be sampled using the inversion method.
ContinuousSampler	Sampler that generates values of type double.
DiscreteInverseCumulativeProbabilityFunction	Interface for a discrete distribution that can be sampled using the inversion method.
DiscreteSampler	Sampler that generates values of type int.
NormalizedGaussianSampler	Marker interface for a sampler that generates values from an N(0,1) Gaussian distribution.
SharedStateContinuousSampler	Sampler that generates values of type double and can create new instances to sample from the same state with a given source of randomness.
SharedStateDiscreteSampler	Sampler that generates values of type int and can create new instances to sample from the same state with a given source of randomness.

Class Summary

Class	Description
AhrensDieterExponentialSampler	Sampling from an exponential distribution.
AhrensDieterMarsagliaTsangGammaSampler	Sampling from the gamma distribution.
AliasMethodDiscreteSampler	Distribution sampler that uses the Alias method.
BoxMullerGaussianSampler	Deprecated. Since version 1.1.
BoxMullerLogNormalSampler	Deprecated. Since version 1.1.
BoxMullerNormalizedGaussianSampler	Box-Muller algorithm for sampling from Gaussian distribution with mean 0 and standard deviation 1.
ChengBetaSampler	Sampling from a beta distribution.
ContinuousUniformSampler	Sampling from a uniform distribution.
DiscreteUniformSampler	Discrete uniform distribution sampler.
GaussianSampler	Sampling from a Gaussian distribution with given mean and standard deviation.
GeometricSampler	Sampling from a geometric distribution.
GuideTableDiscreteSampler	Compute a sample from n values each with an associated probability.
InverseTransformContinuousSampler	Distribution sampler that uses the inversion method.
InverseTransformDiscreteSampler	Distribution sampler that uses the inversion method.
InverseTransformParetoSampler	Sampling from a Pareto distribution.
KempSmallMeanPoissonSampler	Sampler for the Poisson distribution.
LargeMeanPoissonSampler	Sampler for the Poisson distribution.
LogNormalSampler	Sampling from a log-normal distribution.
MarsagliaNormalizedGaussianSampler	Marsaglia polar method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.
MarsagliaTsangWangDiscreteSampler	Sampler for a discrete distribution using an optimised look-up table.
MarsagliaTsangWangDiscreteSampler.Binomial	Create a sampler for the Binomial distribution.
MarsagliaTsangWangDiscreteSampler.Enumerated	Create a sampler for an enumerated distribution of n values each with an associated probability.
MarsagliaTsangWangDiscreteSampler.Poisson	Create a sampler for the Poisson distribution.
PoissonSampler	Sampler for the Poisson distribution.
PoissonSamplerCache	Create a sampler for the Poisson distribution using a cache to minimise construction cost.
RejectionInversionZipfSampler	Implementation of the Zipf distribution.
SamplerBase	Deprecated. Since version 1.1.
SmallMeanPoissonSampler	Sampler for the Poisson distribution.
ZigguratNormalizedGaussianSampler	Marsaglia and Tsang "Ziggurat" method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.