| AhrensDieterMarsagliaTsangGammaSampler.AhrensDieterGammaSampler |
Class to sample from the Gamma distribution when 0 < alpha < 1.
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| AhrensDieterMarsagliaTsangGammaSampler.BaseGammaSampler |
Base class for a sampler from the Gamma distribution.
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| AhrensDieterMarsagliaTsangGammaSampler.MarsagliaTsangGammaSampler |
Class to sample from the Gamma distribution when the alpha >= 1.
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| AliasMethodDiscreteSampler |
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| ChengBetaSampler.BaseChengBetaSampler |
Base class to implement Cheng's algorithms for the beta distribution.
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| ChengBetaSampler.ChengBBBetaSampler |
Computes one sample using Cheng's BB algorithm, when beta distribution alpha and
beta shape parameters are both larger than 1.
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| 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.
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| ContinuousInverseCumulativeProbabilityFunction |
Interface for a continuous distribution that can be sampled using
the
inversion method. |
| ContinuousSampler |
Sampler that generates values of type double.
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| ContinuousUniformSampler |
Sampling from a uniform distribution.
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| DirichletSampler |
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| DirichletSampler.GeneralDirichletSampler |
Sample from a Dirichlet distribution with different concentration parameters
for each category.
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| DirichletSampler.SymmetricDirichletSampler |
Sample from a symmetric Dirichlet distribution with the same concentration parameter
for each category.
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| DiscreteInverseCumulativeProbabilityFunction |
Interface for a discrete distribution that can be sampled using
the
inversion method. |
| DiscreteSampler |
Sampler that generates values of type int.
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| DiscreteUniformSampler.AbstractDiscreteUniformSampler |
Base class for a sampler from a discrete uniform distribution.
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| DiscreteUniformSampler.PowerOf2RangeDiscreteUniformSampler |
Discrete uniform distribution sampler when the range is a power of 2 and greater than 1.
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| DiscreteUniformSampler.SmallRangeDiscreteUniformSampler |
Discrete uniform distribution sampler when the range is small
enough to fit in a positive integer.
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| FastLoadedDiceRollerDiscreteSampler |
Distribution sampler that uses the Fast Loaded Dice Roller (FLDR).
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| FastLoadedDiceRollerDiscreteSampler.FLDRSampler |
Class to implement the FLDR sample algorithm.
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| GeometricSampler.GeometricExponentialSampler |
Sample from the geometric distribution by using a related exponential distribution.
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| GeometricSampler.GeometricP1Sampler |
Sample from the geometric distribution when the probability of success is 1.
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| InternalUtils.FactorialLog |
Class for computing the natural logarithm of the factorial of n.
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| InverseTransformParetoSampler |
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| LargeMeanPoissonSampler |
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| LargeMeanPoissonSampler.LargeMeanPoissonSamplerState |
Encapsulate the state of the sampler.
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| LevySampler |
Sampling from a Lévy distribution.
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| LogNormalSampler |
Sampling from a log-normal distribution.
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| LongSampler |
Sampler that generates values of type long.
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| MarsagliaTsangWangDiscreteSampler.AbstractMarsagliaTsangWangDiscreteSampler |
The base class for Marsaglia-Tsang-Wang samplers.
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| MarsagliaTsangWangDiscreteSampler.MarsagliaTsangWangBase64Int16DiscreteSampler |
An implementation for the sample algorithm based on the decomposition of the
index in the range [0,2^30) into 5 base-64 digits with 16-bit backing storage.
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| MarsagliaTsangWangDiscreteSampler.MarsagliaTsangWangBase64Int32DiscreteSampler |
An implementation for the sample algorithm based on the decomposition of the
index in the range [0,2^30) into 5 base-64 digits with 32-bit backing storage.
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| MarsagliaTsangWangDiscreteSampler.MarsagliaTsangWangBase64Int8DiscreteSampler |
An implementation for the sample algorithm based on the decomposition of the
index in the range [0,2^30) into 5 base-64 digits with 8-bit backing storage.
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| NormalizedGaussianSampler |
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| PoissonSamplerCache |
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| RejectionInversionZipfSampler.RejectionInversionZipfSamplerImpl |
Implements the rejection-inversion method for the Zipf distribution.
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| SamplerBase |
Deprecated.
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| 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.
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| SharedStateDiscreteSampler |
Sampler that generates values of type int and can create new instances to sample
from the same state with a given source of randomness.
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| SharedStateLongSampler |
Sampler that generates values of type long and can create new instances to sample
from the same state with a given source of randomness.
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| SmallMeanPoissonSampler |
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| StableSampler |
Samples from a stable distribution.
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| StableSampler.Alpha1CMSStableSampler |
Implement the stable distribution case: alpha == 1 and beta != 0.
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| StableSampler.BaseStableSampler |
Base class for implementations of a stable distribution that requires an exponential
random deviate.
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| StableSampler.Beta0CMSStableSampler |
Implement the generic stable distribution case: alpha < 2 and beta == 0.
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| StableSampler.Beta0WeronStableSampler |
Implement the generic stable distribution case: alpha < 2 and beta == 0.
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| StableSampler.CauchyStableSampler |
Implement the alpha = 1 and beta = 0 stable distribution case
(Cauchy distribution).
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| StableSampler.CMSStableSampler |
Implement the generic stable distribution case: alpha < 2 and
beta != 0.
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| StableSampler.GaussianStableSampler |
Implement the alpha = 2 stable distribution case (Gaussian distribution).
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| StableSampler.LevyStableSampler |
Implement the alpha = 0.5 and beta = 1 stable distribution case
(Levy distribution).
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| StableSampler.WeronStableSampler |
Implement the generic stable distribution case: alpha < 2 and
beta != 0.
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| TSampler |
Sampling from a T distribution.
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| TSampler.NormalTSampler |
Sample from a t-distribution using a normal distribution.
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| TSampler.StudentsTSampler |
Sample from a t-distribution using Bailey's algorithm.
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| UniformLongSampler |
Discrete uniform distribution sampler generating values of type long.
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| UniformLongSampler.PowerOf2RangeUniformLongSampler |
Discrete uniform distribution sampler when the range is a power of 2 and greater than 1.
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| UniformLongSampler.SmallRangeUniformLongSampler |
Discrete uniform distribution sampler when the range is small
enough to fit in a positive long.
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| ZigguratSampler |
Modified ziggurat method for sampling from Gaussian and exponential distributions.
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| ZigguratSampler.Exponential |
Modified ziggurat method for sampling from an exponential distribution.
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| ZigguratSampler.Exponential.ExponentialMean |
Specialisation which multiplies the standard exponential result by a specified mean.
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| ZigguratSampler.NormalizedGaussian |
Modified ziggurat method for sampling from a Gaussian distribution with
mean 0 and standard deviation 1.
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