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

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.

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.
class	BoxMullerNormalizedGaussianSampler	Box-Muller algorithm for sampling from Gaussian distribution with mean 0 and standard deviation 1.
class	ChengBetaSampler	Sampling from a beta distribution.
class	ContinuousUniformSampler	Sampling from a uniform distribution.
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.
class	TSampler	Sampling from a T distribution.
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.
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.

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
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.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.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.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.