## Package gov.sandia.cognition.statistics.distribution

Provides statistical distributions.

See:
Description

Class Summary
BernoulliDistribution A Bernoulli distribution, which takes a value of "1" with probability "p" and value of "0" with probability "1-p".
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 Beta-family of probability distributions.
BetaDistribution.CDF CDF of the Beta-family 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 one-of-N 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 Chi-Square Distribution.
ChiSquareDistribution.CDF Cumulative Distribution Function (CDF) of a Chi-Square Distribution
ChiSquareDistribution.PDF PDF of the Chi-Square 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.
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 Closed-form 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.CDF CDF of the inverseRootFinder-gamma distribution.
InverseGammaDistribution.PDF PDF of the inverseRootFinder-Gamma distribution.
InverseWishartDistribution The Inverse-Wishart distribution is the multivariate generalization of the inverse-gamma distribution.
InverseWishartDistribution.MultivariateGammaFunction Multivariate generalization of the Gamma function.
InverseWishartDistribution.PDF PDF of the Inverse-Wishart distribution, though I have absolutely no idea why anybody would evaluate the PDF of an Inverse-Wishart...
KolmogorovDistribution Contains the Cumulative Distribution Function description for the "D" statistic used within the Kolmogorov-Smirnov test.
KolmogorovDistribution.CDF Contains the Cumulative Distribution Function description for the "D" statistic used within the Kolmogorov-Smirnov 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 log-odds of a binary event.
LogisticDistribution.CDF CDF of the LogisticDistribution
LogisticDistribution.PDF PDF of the LogisticDistribution
LogNormalDistribution Log-Normal distribution PDF and CDF implementations.
LogNormalDistribution.CDF CDF of the Log-Normal Distribution
LogNormalDistribution.MaximumLikelihoodEstimator Maximum Likelihood Estimator of a log-normal distribution.
LogNormalDistribution.PDF PDF of a Log-normal 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 Expectation-Maximization based "soft" assignment learner.
MixtureOfGaussians.Learner A hard-assignment 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 Inverse-Gamma 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 Dirichlet-Multinomial 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.
MultivariateStudentTDistribution Multivariate generalization of the noncentral Student's t-distribution.
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 inverse-gamma distribution is the product of a univariate Gaussian distribution with an inverse-gamma distribution.
NormalInverseWishartDistribution The normal inverse Wishart distribution
NormalInverseWishartDistribution.PDF PDF of the normal inverse-Wishart 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.
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 F-distribution (also known as Fisher F-distribution, Fisher-Snedecor F-distribution, or just plain old F-distribution).
SnedecorFDistribution.CDF CDF of the F-distribution.
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 Student-t Distribution.
StudentTDistribution.CDF Evaluator that computes the Cumulative Distribution Function (CDF) of a Student-t distribution with a fixed number of degrees of freedom
StudentTDistribution.MaximumLikelihoodEstimator Estimates the parameters of the Student-t 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 Student-t 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 log-normal 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 Yule-Simon distribution is a model of preferential attachment, such as a model of the number of groups follows a power-law distribution (Zipf's Law).
YuleSimonDistribution.CDF CDF of the Yule-Simon Distribution
YuleSimonDistribution.PMF PMF of the Yule-Simon Distribution

## Package gov.sandia.cognition.statistics.distribution Description

Provides statistical distributions.

Since:
2.0
Author:
Justin Basilico