Uses of Interface
org.apache.commons.rng.sampling.distribution.SharedStateContinuousSampler

Packages that use SharedStateContinuousSampler

Package	Description
org.apache.commons.rng.sampling	This package provides sampling utilities.
org.apache.commons.rng.sampling.distribution	This package contains classes for sampling from statistical distributions.

Uses of SharedStateContinuousSampler in org.apache.commons.rng.sampling

Classes in org.apache.commons.rng.sampling that implement SharedStateContinuousSampler

Modifier and Type	Class	Description
private static final class	CompositeSamplers.SharedStateContinuousSamplerFactory.CompositeSharedStateContinuousSampler	A composite continuous sampler with shared state support.

Subclasses with type arguments of type SharedStateContinuousSampler in org.apache.commons.rng.sampling

Modifier and Type	Class	Description
private static final class	CompositeSamplers.SharedStateContinuousSamplerFactory.CompositeSharedStateContinuousSampler	A composite continuous sampler with shared state support.

Classes in org.apache.commons.rng.sampling that implement interfaces with type arguments of type SharedStateContinuousSampler

Modifier and Type	Class	Description
private static final class	CompositeSamplers.SharedStateContinuousSamplerFactory	A factory for creating a composite SharedStateContinuousSampler.

Methods in org.apache.commons.rng.sampling that return SharedStateContinuousSampler

Modifier and Type	Method	Description
SharedStateContinuousSampler	CompositeSamplers.SharedStateContinuousSamplerFactory.createSampler(DiscreteSampler discreteSampler, List<SharedStateContinuousSampler> samplers)

Methods in org.apache.commons.rng.sampling that return types with arguments of type SharedStateContinuousSampler

Modifier and Type	Method	Description
static CompositeSamplers.Builder<SharedStateContinuousSampler>	CompositeSamplers.newSharedStateContinuousSamplerBuilder()	Create a new builder for a composite SharedStateContinuousSampler.

Method parameters in org.apache.commons.rng.sampling with type arguments of type SharedStateContinuousSampler

Modifier and Type	Method	Description
SharedStateContinuousSampler	CompositeSamplers.SharedStateContinuousSamplerFactory.createSampler(DiscreteSampler discreteSampler, List<SharedStateContinuousSampler> samplers)

Constructor parameters in org.apache.commons.rng.sampling with type arguments of type SharedStateContinuousSampler

Modifier	Constructor	Description
(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 SharedStateContinuousSampler

Modifier and Type	Class	Description
class	AhrensDieterExponentialSampler	Sampling from an exponential distribution.
class	AhrensDieterMarsagliaTsangGammaSampler	Sampling from the gamma distribution.
private static final class	AhrensDieterMarsagliaTsangGammaSampler.AhrensDieterGammaSampler	Class to sample from the Gamma distribution when 0 < alpha < 1.
private static class	AhrensDieterMarsagliaTsangGammaSampler.BaseGammaSampler	Base class for a sampler from the Gamma distribution.
private static final class	AhrensDieterMarsagliaTsangGammaSampler.MarsagliaTsangGammaSampler	Class to sample from the Gamma distribution when the alpha >= 1.
class	BoxMullerNormalizedGaussianSampler	Box-Muller algorithm for sampling from Gaussian distribution with mean 0 and standard deviation 1.
class	ChengBetaSampler	Sampling from a beta distribution.
private static class	ChengBetaSampler.BaseChengBetaSampler	Base class to implement Cheng's algorithms for the beta distribution.
private static final class	ChengBetaSampler.ChengBBBetaSampler	Computes one sample using Cheng's BB algorithm, when beta distribution alpha and beta shape parameters are both larger than 1.
private static final class	ChengBetaSampler.ChengBCBetaSampler	Computes one sample using Cheng's BC algorithm, when at least one of beta distribution alpha or beta shape parameters is smaller than 1.
class	ContinuousUniformSampler	Sampling from a uniform distribution.
private static final class	ContinuousUniformSampler.OpenIntervalContinuousUniformSampler	Specialization to sample from an open interval (lo, hi).
class	GaussianSampler	Sampling from a Gaussian distribution with given mean and standard deviation.
class	InverseTransformContinuousSampler	Distribution sampler that uses the inversion method.
class	InverseTransformParetoSampler	Sampling from a Pareto distribution.
final class	LevySampler	Sampling from a Lévy distribution.
class	LogNormalSampler	Sampling from a log-normal distribution.
class	MarsagliaNormalizedGaussianSampler	Marsaglia polar method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.
class	StableSampler	Samples from a stable distribution.
(package private) static class	StableSampler.Alpha1CMSStableSampler	Implement the stable distribution case: alpha == 1 and beta != 0.
private static class	StableSampler.BaseStableSampler	Base class for implementations of a stable distribution that requires an exponential random deviate.
(package private) static class	StableSampler.Beta0CMSStableSampler	Implement the generic stable distribution case: alpha < 2 and beta == 0.
(package private) static class	StableSampler.Beta0WeronStableSampler	Implement the generic stable distribution case: alpha < 2 and beta == 0.
private static final class	StableSampler.CauchyStableSampler	Implement the alpha = 1 and beta = 0 stable distribution case (Cauchy distribution).
(package private) static class	StableSampler.CMSStableSampler	Implement the generic stable distribution case: alpha < 2 and beta != 0.
private static final class	StableSampler.GaussianStableSampler	Implement the alpha = 2 stable distribution case (Gaussian distribution).
private static final class	StableSampler.LevyStableSampler	Implement the alpha = 0.5 and beta = 1 stable distribution case (Levy distribution).
private static final class	StableSampler.TransformedStableSampler	Class for implementations of a stable distribution transformed by scale and location.
(package private) static class	StableSampler.WeronStableSampler	Implement the generic stable distribution case: alpha < 2 and beta != 0.
class	TSampler	Sampling from a T distribution.
private static final class	TSampler.NormalTSampler	Sample from a t-distribution using a normal distribution.
private static final class	TSampler.StudentsTSampler	Sample from a t-distribution using Bailey's algorithm.
class	ZigguratNormalizedGaussianSampler	Marsaglia and Tsang "Ziggurat" method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.
class	ZigguratSampler	Modified ziggurat method for sampling from Gaussian and exponential distributions.
static class	ZigguratSampler.Exponential	Modified ziggurat method for sampling from an exponential distribution.
private static final class	ZigguratSampler.Exponential.ExponentialMean	Specialisation which multiplies the standard exponential result by a specified mean.
static final class	ZigguratSampler.NormalizedGaussian	Modified ziggurat method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.

Subinterfaces with type arguments of type SharedStateContinuousSampler in org.apache.commons.rng.sampling.distribution

Modifier and Type	Interface	Description
interface	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.

Fields in org.apache.commons.rng.sampling.distribution declared as SharedStateContinuousSampler

Modifier and Type	Field	Description
private 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 SharedStateContinuousSampler

Modifier and Type	Method	Description
static <S extends NormalizedGaussianSampler & SharedStateContinuousSampler> S	BoxMullerNormalizedGaussianSampler.of(UniformRandomProvider rng)	Create a new normalised Gaussian sampler.
static <S extends NormalizedGaussianSampler & SharedStateContinuousSampler> S	MarsagliaNormalizedGaussianSampler.of(UniformRandomProvider rng)	Create a new normalised Gaussian sampler.
static <S extends NormalizedGaussianSampler & SharedStateContinuousSampler> S	ZigguratNormalizedGaussianSampler.of(UniformRandomProvider rng)	Create a new normalised Gaussian sampler.

Methods in org.apache.commons.rng.sampling.distribution that return SharedStateContinuousSampler

Modifier and Type	Method	Description
private 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.
SharedStateContinuousSampler	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.
SharedStateContinuousSampler	AhrensDieterMarsagliaTsangGammaSampler.AhrensDieterGammaSampler.withUniformRandomProvider(UniformRandomProvider rng)
SharedStateContinuousSampler	AhrensDieterMarsagliaTsangGammaSampler.MarsagliaTsangGammaSampler.withUniformRandomProvider(UniformRandomProvider rng)
SharedStateContinuousSampler	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.
SharedStateContinuousSampler	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.
SharedStateContinuousSampler	ChengBetaSampler.ChengBBBetaSampler.withUniformRandomProvider(UniformRandomProvider rng)
SharedStateContinuousSampler	ChengBetaSampler.ChengBCBetaSampler.withUniformRandomProvider(UniformRandomProvider rng)
SharedStateContinuousSampler	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.
SharedStateContinuousSampler	ContinuousUniformSampler.OpenIntervalContinuousUniformSampler.withUniformRandomProvider(UniformRandomProvider rng)
SharedStateContinuousSampler	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.
SharedStateContinuousSampler	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.
SharedStateContinuousSampler	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.
SharedStateContinuousSampler	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.
SharedStateContinuousSampler	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.
SharedStateContinuousSampler	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.
SharedStateContinuousSampler	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 SharedStateContinuousSampler

Modifier	Constructor	Description
private	ChengBetaSampler(SharedStateContinuousSampler delegate)
(package private)	GeneralDirichletSampler(UniformRandomProvider rng, SharedStateContinuousSampler[] samplers)
(package private)	SymmetricDirichletSampler(UniformRandomProvider rng, int k, SharedStateContinuousSampler sampler)