See: Description
Class  Description 

BernoulliDistribution 
A Bernoulli distribution, which takes a value of "1" with probability "p"
and value of "0" with probability "1p".

BernoulliDistribution.CDF 
CDF of a Bernoulli distribution.

BernoulliDistribution.PMF 
PMF of the Bernoulli distribution.

BetaBinomialDistribution 
A Binomial distribution where the binomial parameter, p, is set according
to a Beta distribution instead of a single value.

BetaBinomialDistribution.CDF 
CDF of BetaBinomialDistribution

BetaBinomialDistribution.MomentMatchingEstimator 
Estimates the parameters of a Betabinomial distribution using the matching
of moments, not maximum likelihood.

BetaBinomialDistribution.PMF 
PMF of the BetaBinomialDistribution

BetaDistribution 
Computes the Betafamily of probability distributions.

BetaDistribution.CDF 
CDF of the Betafamily distribution

BetaDistribution.MomentMatchingEstimator 
Estimates the parameters of a Beta distribution using the matching
of moments, not maximum likelihood.

BetaDistribution.PDF 
Beta distribution probability density function

BetaDistribution.WeightedMomentMatchingEstimator 
Estimates the parameters of a Beta distribution using the matching
of moments, not maximum likelihood.

BinomialDistribution 
Binomial distribution, which is a collection of Bernoulli trials

BinomialDistribution.CDF 
CDF of the Binomial distribution, which is the probability of getting
up to "x" successes in "N" trials with a Bernoulli probability of "p"

BinomialDistribution.MaximumLikelihoodEstimator 
Maximum likelihood estimator of the distribution

BinomialDistribution.PMF 
The Probability Mass Function of a binomial distribution.

CategoricalDistribution 
The Categorical Distribution is the multivariate generalization of the
Bernoulli distribution, where the outcome of an experiment is a oneofN
output, where the output is a selector Vector.

CategoricalDistribution.PMF 
PMF of the Categorical Distribution

CauchyDistribution 
A Cauchy Distribution is the ratio of two Gaussian Distributions, sometimes
known as the Lorentz distribution.

CauchyDistribution.CDF 
CDF of the CauchyDistribution.

CauchyDistribution.PDF 
PDF of the CauchyDistribution.

ChineseRestaurantProcess 
A Chinese Restaurant Process is a discrete stochastic processes that
partitions data points to clusters.

ChineseRestaurantProcess.PMF 
PMF of the Chinese Restaurant Process

ChiSquareDistribution 
Describes a ChiSquare Distribution.

ChiSquareDistribution.CDF 
Cumulative Distribution Function (CDF) of a ChiSquare Distribution

ChiSquareDistribution.PDF 
PDF of the ChiSquare distribution

DataCountTreeSetBinnedMapHistogram<ValueType extends java.lang.Comparable<? super ValueType>> 
The
DataCountTreeSetBinnedMapHistogram class extends a
DefaultDataDistribution by mapping values to user defined bins
using a TreeSetBinner . 
DefaultDataDistribution<KeyType> 
A default implementation of
ScalarDataDistribution that uses a
backing map. 
DefaultDataDistribution.DefaultFactory<DataType> 
A factory for
DefaultDataDistribution objects using some given
initial capacity for them. 
DefaultDataDistribution.Estimator<KeyType> 
Estimator for a DefaultDataDistribution

DefaultDataDistribution.PMF<KeyType> 
PMF of the DefaultDataDistribution

DefaultDataDistribution.WeightedEstimator<KeyType> 
A weighted estimator for a DefaultDataDistribution

DeterministicDistribution 
A deterministic distribution that returns samples at a single point.

DeterministicDistribution.CDF 
CDF of the deterministic distribution.

DeterministicDistribution.PMF 
PMF of the deterministic distribution.

DirichletDistribution 
The Dirichlet distribution is the multivariate generalization of the beta
distribution.

DirichletDistribution.PDF 
PDF of the Dirichlet distribution.

ExponentialDistribution 
An Exponential distribution describes the time between events in a poisson
process, resulting in a memoryless distribution.

ExponentialDistribution.CDF 
CDF of the ExponentialDistribution.

ExponentialDistribution.MaximumLikelihoodEstimator 
Creates a ExponentialDistribution from data

ExponentialDistribution.PDF 
PDF of the ExponentialDistribution.

ExponentialDistribution.WeightedMaximumLikelihoodEstimator 
Creates a ExponentialDistribution from weighted data

GammaDistribution 
Class representing the Gamma distribution.

GammaDistribution.CDF 
CDF of the Gamma distribution

GammaDistribution.MomentMatchingEstimator 
Computes the parameters of a Gamma distribution by the
Method of Moments

GammaDistribution.PDF 
Closedform PDF of the Gamma distribution

GammaDistribution.WeightedMomentMatchingEstimator 
Estimates the parameters of a Gamma distribution using the matching
of moments, not maximum likelihood.

GeometricDistribution 
The geometric distribution models the number of successes before the first
failure occurs under an independent succession of Bernoulli tests.

GeometricDistribution.CDF 
CDF of the Geometric distribution

GeometricDistribution.MaximumLikelihoodEstimator 
Maximum likelihood estimator of the distribution

GeometricDistribution.PMF 
PMF of the Geometric distribution

InverseGammaDistribution 
Defines an inversegamma distribution.

InverseGammaDistribution.CDF 
CDF of the inverseRootFindergamma distribution.

InverseGammaDistribution.PDF 
PDF of the inverseRootFinderGamma distribution.

InverseWishartDistribution 
The InverseWishart distribution is the multivariate generalization of the
inversegamma distribution.

InverseWishartDistribution.MultivariateGammaFunction 
Multivariate generalization of the Gamma function.

InverseWishartDistribution.PDF 
PDF of the InverseWishart distribution, though I have absolutely no
idea why anybody would evaluate the PDF of an InverseWishart...

KolmogorovDistribution 
Contains the Cumulative Distribution Function description for the "D"
statistic used within the KolmogorovSmirnov test.

KolmogorovDistribution.CDF 
Contains the Cumulative Distribution Function description for the "D"
statistic used within the KolmogorovSmirnov test.

LaplaceDistribution 
A Laplace distribution, sometimes called a double exponential distribution.

LaplaceDistribution.CDF 
CDF of the Laplace distribution.

LaplaceDistribution.MaximumLikelihoodEstimator 
Estimates the ML parameters of a Laplace distribution from a
Collection of Numbers.

LaplaceDistribution.PDF 
The PDF of a Laplace Distribution.

LaplaceDistribution.WeightedMaximumLikelihoodEstimator 
Creates a UnivariateGaussian from weighted data

LinearMixtureModel<DataType,DistributionType extends Distribution<DataType>> 
A linear mixture of RandomVariables, with a prior probability distribution.

LogisticDistribution 
A implementation of the scalar logistic distribution, which measures the
logodds of a binary event.

LogisticDistribution.CDF 
CDF of the LogisticDistribution

LogisticDistribution.PDF 
PDF of the LogisticDistribution

LogNormalDistribution 
LogNormal distribution PDF and CDF implementations.

LogNormalDistribution.CDF 
CDF of the LogNormal Distribution

LogNormalDistribution.MaximumLikelihoodEstimator 
Maximum Likelihood Estimator of a lognormal distribution.

LogNormalDistribution.PDF 
PDF of a Lognormal distribution

LogNormalDistribution.WeightedMaximumLikelihoodEstimator 
Maximum Likelihood Estimator from weighted data

MixtureOfGaussians 
Creates a probability density function (pdf) comprising of a collection of
MultivariateGaussian and corresponding prior probability distribution that
a particular MultivariateGaussian generates observations.

MixtureOfGaussians.EMLearner 
An ExpectationMaximization based "soft" assignment learner.

MixtureOfGaussians.Learner 
A hardassignment learner for a MixtureOfGaussians

MixtureOfGaussians.PDF 
PDF of the MixtureOfGaussians

MultinomialDistribution 
A multinomial distribution is the multivariate/multiclass generalization
of the Binomial distribution.

MultinomialDistribution.Domain 
Allows the iteration through the set of subsets.

MultinomialDistribution.Domain.MultinomialIterator 
An Iterator over a Domain

MultinomialDistribution.PMF 
Probability Mass Function of the Multinomial Distribution.

MultivariateGaussian 
The MultivariateGaussian class implements a multidimensional Gaussian
distribution that contains a mean vector and a covariance matrix.

MultivariateGaussian.IncrementalEstimator 
The estimator that creates a MultivariateGaussian from a stream of
values.

MultivariateGaussian.IncrementalEstimatorCovarianceInverse 
The estimator that creates a MultivariateGaussian from a stream of values
by estimating the mean and covariance inverse (as opposed to the
covariance directly), without ever performing a matrix inversion.

MultivariateGaussian.MaximumLikelihoodEstimator 
Computes the Maximum Likelihood Estimate of the MultivariateGaussian
given a set of Vectors

MultivariateGaussian.PDF 
PDF of a multivariate Gaussian

MultivariateGaussian.SufficientStatistic 
Implements the sufficient statistics of the MultivariateGaussian.

MultivariateGaussian.SufficientStatisticCovarianceInverse 
Implements the sufficient statistics of the MultivariateGaussian while
estimating the inverse of the covariance matrix.

MultivariateGaussian.WeightedMaximumLikelihoodEstimator 
Computes the Weighted Maximum Likelihood Estimate of the
MultivariateGaussian given a weighted set of Vectors

MultivariateGaussianInverseGammaDistribution 
A distribution where the mean is selected by a multivariate Gaussian and
a variance parameter (either for a univariate Gaussian or isotropic Gaussian)
is determined by an InverseGamma distribution.

MultivariateMixtureDensityModel<DistributionType extends ClosedFormComputableDistribution<Vector>> 
A LinearMixtureModel of multivariate distributions with associated PDFs.

MultivariateMixtureDensityModel.PDF<DistributionType extends ClosedFormComputableDistribution<Vector>> 
PDF of the MultivariateMixtureDensityModel

MultivariatePolyaDistribution 
A multivariate Polya Distribution, also known as a DirichletMultinomial
model, is a compound distribution where the parameters of a multinomial
are drawn from a Dirichlet distribution with fixed parameters and a constant
number of trials and then the observations are generated by this
multinomial.

MultivariatePolyaDistribution.PMF 
PMF of the MultivariatePolyaDistribution

MultivariateStudentTDistribution 
Multivariate generalization of the noncentral Student's tdistribution.

MultivariateStudentTDistribution.PDF 
PDF of the MultivariateStudentTDistribution

NegativeBinomialDistribution 
Negative binomial distribution, also known as the Polya distribution,
gives the number of successes of a series of Bernoulli trials before
recording a given number of failures.

NegativeBinomialDistribution.CDF 
CDF of the NegativeBinomialDistribution

NegativeBinomialDistribution.MaximumLikelihoodEstimator 
Maximum likelihood estimator of the distribution

NegativeBinomialDistribution.PMF 
PMF of the NegativeBinomialDistribution.

NegativeBinomialDistribution.WeightedMaximumLikelihoodEstimator 
Weighted maximum likelihood estimator of the distribution

NormalInverseGammaDistribution 
The normal inversegamma distribution is the product of a univariate
Gaussian distribution with an inversegamma distribution.

NormalInverseGammaDistribution.PDF 
PDF of the NormalInverseGammaDistribution

NormalInverseWishartDistribution 
The normal inverse Wishart distribution

NormalInverseWishartDistribution.PDF 
PDF of the normal inverseWishart distribution.

ParetoDistribution 
This class describes the Pareto distribution, sometimes called the Bradford
Distribution.

ParetoDistribution.CDF 
CDF of the Pareto Distribution.

ParetoDistribution.PDF 
PDF of the ParetoDistribution

PoissonDistribution 
A Poisson distribution is the limits of what happens when a Bernoulli trial
with "rare" events are sampled on a continuous basis and then binned into
discrete time intervals.

PoissonDistribution.CDF 
CDF of the PoissonDistribution

PoissonDistribution.MaximumLikelihoodEstimator 
Creates a PoissonDistribution from data

PoissonDistribution.PMF 
PMF of the PoissonDistribution.

PoissonDistribution.WeightedMaximumLikelihoodEstimator 
Creates a PoissonDistribution from weighted data.

ScalarDataDistribution 
A Data Distribution that uses Doubles as its keys, making it a univariate
distribution

ScalarDataDistribution.CDF 
CDF of the ScalarDataDistribution, maintains the keys/domain in
sorted order (TreeMap), so it's slower than it's peers.

ScalarDataDistribution.Estimator 
Estimator for a ScalarDataDistribution

ScalarDataDistribution.PMF 
PMF of the ScalarDataDistribution

ScalarMixtureDensityModel 
ScalarMixtureDensityModel (SMDM) implements just that: a scalar mixture density
model.

ScalarMixtureDensityModel.CDF 
CDFof the SMDM

ScalarMixtureDensityModel.EMLearner 
An EM learner that estimates a mixture model from data

ScalarMixtureDensityModel.PDF 
PDF of the SMDM

SnedecorFDistribution 
CDF of the Snedecor Fdistribution (also known as Fisher Fdistribution,
FisherSnedecor Fdistribution, or just plain old Fdistribution).

SnedecorFDistribution.CDF 
CDF of the Fdistribution.

StudentizedRangeDistribution 
Implementation of the Studentized Range distribution, which defines the
population correction factor when performing multiple comparisons.

StudentizedRangeDistribution.APStat 
This is a translation of Fortran code taken from
http://lib.stat.cmu.edu/apstat/, and the comments on the individual functions
in this class are taken directly from the original.

StudentizedRangeDistribution.CDF 
CDF of the StudentizedRangeDistribution

StudentizedRangeDistribution.SampleRange 
Computes the estimate of the Studentized Range for a single sample

StudentTDistribution 
Defines a noncentral Studentt Distribution.

StudentTDistribution.CDF 
Evaluator that computes the Cumulative Distribution Function (CDF) of
a Studentt distribution with a fixed number of degrees of freedom

StudentTDistribution.MaximumLikelihoodEstimator 
Estimates the parameters of the Studentt distribution from the given
data, where the degrees of freedom are estimated from the Kurtosis
of the sample data.

StudentTDistribution.PDF 
Evaluator that computes the Probability Density Function (CDF) of
a Studentt distribution with a fixed number of degrees of freedom

StudentTDistribution.WeightedMaximumLikelihoodEstimator 
Creates a UnivariateGaussian from weighted data

UniformDistribution 
Contains the (very simple) definition of a continuous Uniform distribution,
parameterized between the minimum and maximum bounds.

UniformDistribution.CDF 
Cumulative Distribution Function of a uniform

UniformDistribution.MaximumLikelihoodEstimator 
Maximum Likelihood Estimator of a uniform distribution.

UniformDistribution.PDF 
Probability density function of a Uniform Distribution

UniformIntegerDistribution 
Contains the (very simple) definition of a continuous Uniform distribution,
parameterized between the minimum and maximum bounds.

UniformIntegerDistribution.CDF 
Implements the cumulative distribution function for the discrete
uniform distribution.

UniformIntegerDistribution.MaximumLikelihoodEstimator 
Implements a maximum likelihood estimator for the discrete uniform
distribution.

UniformIntegerDistribution.PMF 
Probability mass function of a discrete uniform distribution.

UnivariateGaussian 
This class contains internal classes that implement useful functions based
on the Gaussian distribution.

UnivariateGaussian.CDF 
CDF of the underlying Gaussian.

UnivariateGaussian.CDF.Inverse 
Inverts the CumulativeDistribution function.

UnivariateGaussian.ErrorFunction 
Gaussian Error Function, useful for computing the cumulative distribution
function for a Gaussian.

UnivariateGaussian.ErrorFunction.Inverse 
Inverse of the ErrorFunction

UnivariateGaussian.IncrementalEstimator 
Implements an incremental estimator for the sufficient statistics for
a UnivariateGaussian.

UnivariateGaussian.MaximumLikelihoodEstimator 
Creates a UnivariateGaussian from data

UnivariateGaussian.PDF 
PDF of the underlying Gaussian.

UnivariateGaussian.SufficientStatistic 
Captures the sufficient statistics of a UnivariateGaussian, which are
the values to estimate the mean and variance.

UnivariateGaussian.WeightedMaximumLikelihoodEstimator 
Creates a UnivariateGaussian from weighted data

WeibullDistribution 
Describes a Weibull distribution, which is often used to describe the
mortality, lifespan, or size distribution of objects.

WeibullDistribution.CDF 
CDF of the Weibull distribution

WeibullDistribution.PDF 
PDF of the Weibull distribution

YuleSimonDistribution 
The YuleSimon distribution is a model of preferential attachment, such as
a model of the number of groups follows a powerlaw distribution
(Zipf's Law).

YuleSimonDistribution.CDF 
CDF of the YuleSimon Distribution

YuleSimonDistribution.PMF 
PMF of the YuleSimon Distribution
