Uses of Interface
org.apache.commons.rng.sampling.distribution.ContinuousSampler
Packages that use ContinuousSampler
Package
Description
This package provides sampling utilities.
This package contains classes for sampling from statistical distributions.
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 ContinuousSamplerModifier and TypeClassDescriptionprivate static final class
A composite continuous sampler.private static final class
A composite continuous sampler with shared state support.Methods in org.apache.commons.rng.sampling that return ContinuousSamplerModifier and TypeMethodDescriptionCompositeSamplers.ContinuousSamplerFactory.createSampler
(DiscreteSampler discreteSampler, List<ContinuousSampler> samplers) Methods in org.apache.commons.rng.sampling that return types with arguments of type ContinuousSamplerModifier and TypeMethodDescriptionCompositeSamplers.newContinuousSamplerBuilder()
Create a new builder for a compositeContinuousSampler
.Method parameters in org.apache.commons.rng.sampling with type arguments of type ContinuousSamplerModifier and TypeMethodDescriptionCompositeSamplers.ContinuousSamplerFactory.createSampler
(DiscreteSampler discreteSampler, List<ContinuousSampler> samplers) Constructor parameters in org.apache.commons.rng.sampling with type arguments of type ContinuousSamplerModifierConstructorDescription(package private)
CompositeContinuousSampler
(DiscreteSampler discreteSampler, List<ContinuousSampler> samplers) -
Uses of ContinuousSampler in org.apache.commons.rng.sampling.distribution
Subinterfaces of ContinuousSampler in org.apache.commons.rng.sampling.distributionModifier and TypeInterfaceDescriptioninterface
Marker interface for a sampler that generates values from an N(0,1) Gaussian distribution.interface
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 ContinuousSamplerModifier and TypeClassDescriptionclass
Sampling from an exponential distribution.class
Sampling from the gamma distribution.private static final 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 final class
Class to sample from the Gamma distribution when thealpha >= 1
.class
Deprecated.Since version 1.1.class
Deprecated.Since version 1.1.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 final class
Computes one sample using Cheng's BB algorithm, when beta distributionalpha
andbeta
shape parameters are both larger than 1.private static final 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 final 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 final 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 final class
Implement thealpha = 2
stable distribution case (Gaussian distribution).private static final class
Implement thealpha = 0.5
andbeta = 1
stable distribution case (Levy distribution).private static final 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 final 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 ContinuousSamplerModifier and TypeFieldDescriptionprivate final ContinuousSampler
StableSampler.BaseStableSampler.expSampler
The exponential sampler.private final 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 ContinuousSamplerModifier and TypeFieldDescriptionprivate final ContinuousSampler
UnitBallSampler.UnitBallSampler3D.exp
The exponential distribution (mean=1).private final ContinuousSampler
UnitBallSampler.UnitBallSamplerND.exp
The exponential distribution (mean=1).