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

Packages that use ContinuousSampler

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.
org.apache.commons.rng.sampling.shape	This package contains classes for sampling coordinates from shapes, for example a unit ball.

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

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

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

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

Modifier and Type	Method	Description
ContinuousSampler	CompositeSamplers.ContinuousSamplerFactory.createSampler(DiscreteSampler discreteSampler, java.util.List<ContinuousSampler> samplers)

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

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

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

Modifier and Type	Method	Description
ContinuousSampler	CompositeSamplers.ContinuousSamplerFactory.createSampler(DiscreteSampler discreteSampler, java.util.List<ContinuousSampler> samplers)

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

Constructor	Description
CompositeContinuousSampler(DiscreteSampler discreteSampler, java.util.List<ContinuousSampler> samplers)

Uses of ContinuousSampler in org.apache.commons.rng.sampling.distribution

Subinterfaces of ContinuousSampler in org.apache.commons.rng.sampling.distribution

Modifier and Type	Interface	Description
interface	NormalizedGaussianSampler	Marker interface for a sampler that generates values from an N(0,1) Gaussian distribution.
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.

Classes in org.apache.commons.rng.sampling.distribution that implement ContinuousSampler

Modifier and Type	Class	Description
class	AhrensDieterExponentialSampler	Sampling from an exponential distribution.
class	AhrensDieterMarsagliaTsangGammaSampler	Sampling from the gamma distribution.
private static 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 class	AhrensDieterMarsagliaTsangGammaSampler.MarsagliaTsangGammaSampler	Class to sample from the Gamma distribution when the alpha >= 1.
class	BoxMullerGaussianSampler	Deprecated. Since version 1.1.
class	BoxMullerLogNormalSampler	Deprecated. Since version 1.1.
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 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 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 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.
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 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 class	StableSampler.GaussianStableSampler	Implement the alpha = 2 stable distribution case (Gaussian distribution).
private static class	StableSampler.LevyStableSampler	Implement the alpha = 0.5 and beta = 1 stable distribution case (Levy distribution).
private static 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 class	TSampler.NormalTSampler	Sample from a t-distribution using a normal distribution.
private static 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 class	ZigguratSampler.Exponential.ExponentialMean	Specialisation which multiplies the standard exponential result by a specified mean.
static class	ZigguratSampler.NormalizedGaussian	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 ContinuousSampler

Modifier and Type	Field	Description
private ContinuousSampler	StableSampler.BaseStableSampler.expSampler	The exponential sampler.
private ContinuousSampler	BoxMullerLogNormalSampler.sampler	Deprecated. Delegate.

Uses of ContinuousSampler in org.apache.commons.rng.sampling.shape

Fields in org.apache.commons.rng.sampling.shape declared as ContinuousSampler

Modifier and Type	Field	Description
private ContinuousSampler	UnitBallSampler.UnitBallSampler3D.exp	The exponential distribution (mean=1).
private ContinuousSampler	UnitBallSampler.UnitBallSamplerND.exp	The exponential distribution (mean=1).