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Description
Class Summary  

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 Beta 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 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 lognormal distribution. 
UniformDistribution.PDF  Probability density function of a 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 
Provides statistical distributions.


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