Uses of Interface
org.apache.commons.rng.sampling.distribution.SharedStateContinuousSampler
Packages that use SharedStateContinuousSampler
Package
Description
This package provides sampling utilities.
This package contains classes for sampling from statistical distributions.
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Uses of SharedStateContinuousSampler in org.apache.commons.rng.sampling
Classes in org.apache.commons.rng.sampling that implement SharedStateContinuousSamplerModifier and TypeClassDescriptionprivate static class
A composite continuous sampler with shared state support.Methods in org.apache.commons.rng.sampling that return SharedStateContinuousSamplerModifier and TypeMethodDescriptionCompositeSamplers.SharedStateContinuousSamplerFactory.createSampler
(DiscreteSampler discreteSampler, List<SharedStateContinuousSampler> samplers) Methods in org.apache.commons.rng.sampling that return types with arguments of type SharedStateContinuousSamplerModifier and TypeMethodDescriptionCompositeSamplers.newSharedStateContinuousSamplerBuilder()
Create a new builder for a compositeSharedStateContinuousSampler
.Method parameters in org.apache.commons.rng.sampling with type arguments of type SharedStateContinuousSamplerModifier and TypeMethodDescriptionCompositeSamplers.SharedStateContinuousSamplerFactory.createSampler
(DiscreteSampler discreteSampler, List<SharedStateContinuousSampler> samplers) Constructor parameters in org.apache.commons.rng.sampling with type arguments of type SharedStateContinuousSamplerModifierConstructorDescription(package private)
CompositeSharedStateContinuousSampler
(SharedStateDiscreteSampler discreteSampler, List<SharedStateContinuousSampler> samplers) -
Uses of SharedStateContinuousSampler in org.apache.commons.rng.sampling.distribution
Classes in org.apache.commons.rng.sampling.distribution that implement SharedStateContinuousSamplerModifier and TypeClassDescriptionclass
Sampling from an exponential distribution.class
Sampling from the gamma distribution.private static class
Class to sample from the Gamma distribution when0 < alpha < 1
.private static class
Base class for a sampler from the Gamma distribution.private static class
Class to sample from the Gamma distribution when thealpha >= 1
.class
Box-Muller algorithm for sampling from Gaussian distribution with mean 0 and standard deviation 1.class
Sampling from a beta distribution.private static class
Base class to implement Cheng's algorithms for the beta distribution.private static class
Computes one sample using Cheng's BB algorithm, when beta distributionalpha
andbeta
shape parameters are both larger than 1.private static class
Computes one sample using Cheng's BC algorithm, when at least one of beta distributionalpha
orbeta
shape parameters is smaller than 1.class
Sampling from a uniform distribution.private static class
Specialization to sample from an open interval(lo, hi)
.class
Sampling from a Gaussian distribution with given mean and standard deviation.class
Distribution sampler that uses the inversion method.class
Sampling from a Pareto distribution.final class
Sampling from a Lévy distribution.class
Sampling from a log-normal distribution.class
Marsaglia polar method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.class
Samples from a stable distribution.(package private) static class
Implement the stable distribution case:alpha == 1
andbeta != 0
.private static class
Base class for implementations of a stable distribution that requires an exponential random deviate.(package private) static class
Implement the generic stable distribution case:alpha < 2
andbeta == 0
.(package private) static class
Implement the generic stable distribution case:alpha < 2
andbeta == 0
.private static class
Implement thealpha = 1
andbeta = 0
stable distribution case (Cauchy distribution).(package private) static class
Implement the generic stable distribution case:alpha < 2
andbeta != 0
.private static class
Implement thealpha = 2
stable distribution case (Gaussian distribution).private static class
Implement thealpha = 0.5
andbeta = 1
stable distribution case (Levy distribution).private static class
Class for implementations of a stable distribution transformed by scale and location.(package private) static class
Implement the generic stable distribution case:alpha < 2
andbeta != 0
.class
Sampling from a T distribution.private static final class
Sample from a t-distribution using a normal distribution.private static final class
Sample from a t-distribution using Bailey's algorithm.class
Marsaglia and Tsang "Ziggurat" method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.class
Modified ziggurat method for sampling from Gaussian and exponential distributions.static class
Modified ziggurat method for sampling from an exponential distribution.private static class
Specialisation which multiplies the standard exponential result by a specified mean.static final class
Modified ziggurat method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.Fields in org.apache.commons.rng.sampling.distribution declared as SharedStateContinuousSamplerModifier and TypeFieldDescriptionprivate final SharedStateContinuousSampler
AhrensDieterMarsagliaTsangGammaSampler.delegate
The appropriate gamma sampler for the parameters.private final SharedStateContinuousSampler
ChengBetaSampler.delegate
The appropriate beta sampler for the parameters.private final SharedStateContinuousSampler
LargeMeanPoissonSampler.exponential
Exponential.private final SharedStateContinuousSampler
ZigguratSampler.NormalizedGaussian.exponential
Exponential sampler used for the long tail.private final SharedStateContinuousSampler
GeometricSampler.GeometricExponentialSampler.exponentialSampler
The related exponential sampler for the geometric distribution.private final SharedStateContinuousSampler
LargeMeanPoissonSampler.gaussian
Gaussian.private final SharedStateContinuousSampler
DirichletSampler.SymmetricDirichletSampler.sampler
Sampler for the categories.private final SharedStateContinuousSampler[]
DirichletSampler.GeneralDirichletSampler.samplers
Samplers for each category.Methods in org.apache.commons.rng.sampling.distribution with type parameters of type SharedStateContinuousSamplerModifier and TypeMethodDescriptionstatic <S extends NormalizedGaussianSampler & SharedStateContinuousSampler>
SBoxMullerNormalizedGaussianSampler.of
(UniformRandomProvider rng) Create a new normalised Gaussian sampler.static <S extends NormalizedGaussianSampler & SharedStateContinuousSampler>
SMarsagliaNormalizedGaussianSampler.of
(UniformRandomProvider rng) Create a new normalised Gaussian sampler.static <S extends NormalizedGaussianSampler & SharedStateContinuousSampler>
SZigguratNormalizedGaussianSampler.of
(UniformRandomProvider rng) Create a new normalised Gaussian sampler.Methods in org.apache.commons.rng.sampling.distribution that return SharedStateContinuousSamplerModifier and TypeMethodDescriptionprivate static SharedStateContinuousSampler
DirichletSampler.createSampler
(UniformRandomProvider rng, double alpha) Creates a gamma sampler for a category with the given concentration parameter.static SharedStateContinuousSampler
AhrensDieterExponentialSampler.of
(UniformRandomProvider rng, double mean) Create a new exponential distribution sampler.static SharedStateContinuousSampler
AhrensDieterMarsagliaTsangGammaSampler.of
(UniformRandomProvider rng, double alpha, double theta) Creates a new gamma distribution sampler.static SharedStateContinuousSampler
ChengBetaSampler.of
(UniformRandomProvider rng, double alpha, double beta) Creates a new beta distribution sampler.static SharedStateContinuousSampler
ContinuousUniformSampler.of
(UniformRandomProvider rng, double lo, double hi) Creates a new continuous uniform distribution sampler.static SharedStateContinuousSampler
ContinuousUniformSampler.of
(UniformRandomProvider rng, double lo, double hi, boolean excludeBounds) Creates a new continuous uniform distribution sampler.static SharedStateContinuousSampler
GaussianSampler.of
(NormalizedGaussianSampler normalized, double mean, double standardDeviation) Create a new normalised Gaussian sampler.static SharedStateContinuousSampler
InverseTransformContinuousSampler.of
(UniformRandomProvider rng, ContinuousInverseCumulativeProbabilityFunction function) Create a new inverse-transform continuous sampler.static SharedStateContinuousSampler
InverseTransformParetoSampler.of
(UniformRandomProvider rng, double scale, double shape) Creates a new Pareto distribution sampler.static SharedStateContinuousSampler
LogNormalSampler.of
(NormalizedGaussianSampler gaussian, double mu, double sigma) Create a new log-normal distribution sampler.AhrensDieterExponentialSampler.withUniformRandomProvider
(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.AhrensDieterMarsagliaTsangGammaSampler.AhrensDieterGammaSampler.withUniformRandomProvider
(UniformRandomProvider rng) AhrensDieterMarsagliaTsangGammaSampler.MarsagliaTsangGammaSampler.withUniformRandomProvider
(UniformRandomProvider rng) AhrensDieterMarsagliaTsangGammaSampler.withUniformRandomProvider
(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.BoxMullerNormalizedGaussianSampler.withUniformRandomProvider
(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.ChengBetaSampler.ChengBBBetaSampler.withUniformRandomProvider
(UniformRandomProvider rng) ChengBetaSampler.ChengBCBetaSampler.withUniformRandomProvider
(UniformRandomProvider rng) ChengBetaSampler.withUniformRandomProvider
(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.ContinuousUniformSampler.OpenIntervalContinuousUniformSampler.withUniformRandomProvider
(UniformRandomProvider rng) ContinuousUniformSampler.withUniformRandomProvider
(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.GaussianSampler.withUniformRandomProvider
(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.InverseTransformContinuousSampler.withUniformRandomProvider
(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.InverseTransformParetoSampler.withUniformRandomProvider
(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.LogNormalSampler.withUniformRandomProvider
(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.MarsagliaNormalizedGaussianSampler.withUniformRandomProvider
(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.ZigguratNormalizedGaussianSampler.withUniformRandomProvider
(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.Constructors in org.apache.commons.rng.sampling.distribution with parameters of type SharedStateContinuousSamplerModifierConstructorDescription(package private)
GeneralDirichletSampler
(UniformRandomProvider rng, SharedStateContinuousSampler[] samplers) (package private)
SymmetricDirichletSampler
(UniformRandomProvider rng, int k, SharedStateContinuousSampler sampler)