Uses of Package
org.apache.commons.rng.sampling.distribution
Packages that use org.apache.commons.rng.sampling.distribution
Package
Description
This package provides sampling utilities.
This package contains classes for sampling from statistical distributions.
This package contains classes for sampling coordinates from shapes, for example a unit ball.
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Classes in org.apache.commons.rng.sampling.distribution used by org.apache.commons.rng.samplingClassDescriptionSampler that generates values of type
double
.Sampler that generates values of typeint
.Sampler that generates values of typelong
.Marker interface for a sampler that generates values from an N(0,1) Gaussian distribution.Sampler that generates values of typedouble
and can create new instances to sample from the same state with a given source of randomness.Sampler that generates values of typeint
and can create new instances to sample from the same state with a given source of randomness.Sampler that generates values of typelong
and can create new instances to sample from the same state with a given source of randomness. -
Classes in org.apache.commons.rng.sampling.distribution used by org.apache.commons.rng.sampling.distributionClassDescriptionClass to sample from the Gamma distribution when
0 < alpha < 1
.Base class for a sampler from the Gamma distribution.Class to sample from the Gamma distribution when thealpha >= 1
.Distribution sampler that uses the Alias method.Base class to implement Cheng's algorithms for the beta distribution.Computes one sample using Cheng's BB algorithm, when beta distributionalpha
andbeta
shape parameters are both larger than 1.Computes one sample using Cheng's BC algorithm, when at least one of beta distributionalpha
orbeta
shape parameters is smaller than 1.Interface for a continuous distribution that can be sampled using the inversion method.Sampler that generates values of typedouble
.Sampling from a uniform distribution.Sampling from a Dirichlet distribution.Sample from a Dirichlet distribution with different concentration parameters for each category.Sample from a symmetric Dirichlet distribution with the same concentration parameter for each category.Interface for a discrete distribution that can be sampled using the inversion method.Sampler that generates values of typeint
.Base class for a sampler from a discrete uniform distribution.Discrete uniform distribution sampler when the range is a power of 2 and greater than 1.Discrete uniform distribution sampler when the range is small enough to fit in a positive integer.Distribution sampler that uses the Fast Loaded Dice Roller (FLDR).Class to implement the FLDR sample algorithm.Sample from the geometric distribution by using a related exponential distribution.Sample from the geometric distribution when the probability of success is 1.Class for computing the natural logarithm of the factorial ofn
.Sampling from a Pareto distribution.Sampler for the Poisson distribution.Encapsulate the state of the sampler.Sampling from a Lévy distribution.Sampling from a log-normal distribution.Sampler that generates values of typelong
.The base class for Marsaglia-Tsang-Wang samplers.An implementation for the sample algorithm based on the decomposition of the index in the range[0,2^30)
into 5 base-64 digits with 16-bit backing storage.An implementation for the sample algorithm based on the decomposition of the index in the range[0,2^30)
into 5 base-64 digits with 32-bit backing storage.An implementation for the sample algorithm based on the decomposition of the index in the range[0,2^30)
into 5 base-64 digits with 8-bit backing storage.Marker interface for a sampler that generates values from an N(0,1) Gaussian distribution.Create a sampler for the Poisson distribution using a cache to minimise construction cost.Implements the rejection-inversion method for the Zipf distribution.Deprecated.Since version 1.1.Sampler that generates values of typedouble
and can create new instances to sample from the same state with a given source of randomness.Sampler that generates values of typeint
and can create new instances to sample from the same state with a given source of randomness.Sampler that generates values of typelong
and can create new instances to sample from the same state with a given source of randomness.Sampler for the Poisson distribution.Samples from a stable distribution.Implement the stable distribution case:alpha == 1
andbeta != 0
.Base class for implementations of a stable distribution that requires an exponential random deviate.Implement the generic stable distribution case:alpha < 2
andbeta == 0
.Implement the generic stable distribution case:alpha < 2
andbeta == 0
.Implement thealpha = 1
andbeta = 0
stable distribution case (Cauchy distribution).Implement the generic stable distribution case:alpha < 2
andbeta != 0
.Implement thealpha = 2
stable distribution case (Gaussian distribution).Implement thealpha = 0.5
andbeta = 1
stable distribution case (Levy distribution).Implement the generic stable distribution case:alpha < 2
andbeta != 0
.Sampling from a T distribution.Sample from a t-distribution using a normal distribution.Sample from a t-distribution using Bailey's algorithm.Discrete uniform distribution sampler generating values of typelong
.Discrete uniform distribution sampler when the range is a power of 2 and greater than 1.Discrete uniform distribution sampler when the range is small enough to fit in a positive long.Modified ziggurat method for sampling from Gaussian and exponential distributions.Modified ziggurat method for sampling from an exponential distribution.Specialisation which multiplies the standard exponential result by a specified mean.Modified ziggurat method for sampling from a Gaussian distribution with mean 0 and standard deviation 1. -
Classes in org.apache.commons.rng.sampling.distribution used by org.apache.commons.rng.sampling.shapeClassDescriptionSampler that generates values of type
double
.Marker interface for a sampler that generates values from an N(0,1) Gaussian distribution.