Uses of Interface
org.apache.commons.rng.sampling.distribution.ContinuousSampler
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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. -
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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 compositeContinuousSampler
.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)
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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 typedouble
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 when0 < 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 thealpha >= 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 distributionalpha
andbeta
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 distributionalpha
orbeta
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
andbeta != 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
andbeta == 0
.(package private) static class
StableSampler.Beta0WeronStableSampler
Implement the generic stable distribution case:alpha < 2
andbeta == 0
.private static class
StableSampler.CauchyStableSampler
Implement thealpha = 1
andbeta = 0
stable distribution case (Cauchy distribution).(package private) static class
StableSampler.CMSStableSampler
Implement the generic stable distribution case:alpha < 2
andbeta != 0
.private static class
StableSampler.GaussianStableSampler
Implement thealpha = 2
stable distribution case (Gaussian distribution).private static class
StableSampler.LevyStableSampler
Implement thealpha = 0.5
andbeta = 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
andbeta != 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).
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