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

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