Uses of Interface
gov.sandia.cognition.statistics.Distribution

Packages that use Distribution

Package	Description
gov.sandia.cognition.learning.algorithm.hmm	Provides hidden Markov model (HMM) algorithms.
gov.sandia.cognition.learning.function.categorization	Provides functions that output a discrete set of categories.
gov.sandia.cognition.statistics	Provides the inheritance hierarchy for general statistical methods and distributions.
gov.sandia.cognition.statistics.bayesian	Provides algorithms for computing Bayesian estimates of parameters.
gov.sandia.cognition.statistics.distribution	Provides statistical distributions.
gov.sandia.cognition.statistics.montecarlo	Provides Monte Carlo procedures for numerical integration and sampling.

Uses of Distribution in gov.sandia.cognition.learning.algorithm.hmm

Classes in gov.sandia.cognition.learning.algorithm.hmm that implement Distribution

Modifier and Type	Class and Description
class	HiddenMarkovModel<ObservationType> A discrete-state Hidden Markov Model (HMM) with either continuous or discrete observations.
class	ParallelHiddenMarkovModel<ObservationType> A Hidden Markov Model with parallelized processing.

Uses of Distribution in gov.sandia.cognition.learning.function.categorization

Classes in gov.sandia.cognition.learning.function.categorization that implement Distribution

Modifier and Type	Class and Description
class	MaximumAPosterioriCategorizer<ObservationType,CategoryType> Categorizer that returns the category with the highest posterior likelihood for a given observation.

Uses of Distribution in gov.sandia.cognition.statistics

Classes in gov.sandia.cognition.statistics with type parameters of type Distribution

Modifier and Type	Class and Description
class	AbstractIncrementalEstimator<DataType,DistributionType extends Distribution<? extends DataType>,SufficientStatisticsType extends SufficientStatistic<DataType,DistributionType>> Partial implementation of IncrementalEstimator.
interface	DistributionEstimator<ObservationType,DistributionType extends Distribution<? extends ObservationType>> A BatchLearner that estimates a Distribution.
interface	DistributionParameter<ParameterType,ConditionalType extends Distribution<?>> Allows access to a parameter within a closed-form distribution, given by the high-level String value.
interface	DistributionWeightedEstimator<ObservationType,DistributionType extends Distribution<? extends ObservationType>> A BatchLearner that estimates a Distribution from a Collection of weighted data.
interface	IncrementalEstimator<DataType,DistributionType extends Distribution<? extends DataType>,SufficientStatisticsType extends SufficientStatistic<? super DataType,? extends DistributionType>> An estimator of a Distribution that uses SufficientStatistic to arrive at its result.

Subinterfaces of Distribution in gov.sandia.cognition.statistics

Modifier and Type	Interface and Description
interface	ClosedFormComputableDiscreteDistribution<DataType> A discrete, closed-form Distribution with a PMF.
interface	ClosedFormComputableDistribution<DataType> A closed-form Distribution that also has an associated distribution function.
interface	ClosedFormCumulativeDistributionFunction<DomainType extends java.lang.Number> Functionality of a cumulative distribution function that's defined with closed-form parameters.
interface	ClosedFormDiscreteUnivariateDistribution<DomainType extends java.lang.Number> A ClosedFormUnivariateDistribution that is also a DiscreteDistribution
interface	ClosedFormDistribution<DataType> Defines a distribution that is described a parameterized mathematical equation.
interface	ClosedFormUnivariateDistribution<NumberType extends java.lang.Number> Defines the functionality associated with a closed-form scalar distribution.
interface	ComputableDistribution<DomainType> A type of Distribution that has an associated distribution function, either a PDF or PMF.
interface	CumulativeDistributionFunction<NumberType extends java.lang.Number> Functionality of a cumulative distribution function.
interface	DataDistribution<DataType> A distribution of data from which we can sample and perform Ring operations.
static interface	DataDistribution.PMF<KeyType> Interface for the probability mass function (PMF) of a data distribution.
interface	DiscreteDistribution<DataType> A Distribution with a countable domain (input) set.
interface	DistributionWithMean<DataType> A Distribution that has a well-defined mean, or first central moment.
interface	EstimableDistribution<ObservationType,DistributionType extends EstimableDistribution<ObservationType,? extends DistributionType>> A Distribution that has an estimator associated with it, typically a closed-form estimator.
interface	EstimableWeightedDistribution<ObservationType,DistributionType extends EstimableWeightedDistribution<ObservationType,? extends DistributionType>> A Distribution that has an estimator associated with it, typically a closed-form estimator, that can estimate the distribution from weighted data.
interface	IntegerDistribution Defines a distribution over natural numbers.
interface	InvertibleCumulativeDistributionFunction<NumberType extends java.lang.Number> A cumulative distribution function that is empirically invertible.
interface	ProbabilityDensityFunction<DataType> Defines a probability density function.
interface	ProbabilityFunction<DataType> A Distribution that has an evaluate method that indicates p(x), such as a probability density function or a probability mass function (but NOT a cumulative distribution function).
interface	ProbabilityMassFunction<DataType> The ProbabilityMassFunction interface defines the functionality of a probability mass function.
interface	RandomVariable<DataType> Describes the functionality of a random variable.
interface	SmoothCumulativeDistributionFunction This defines a CDF that has an associated derivative, which is its PDF.
interface	SmoothUnivariateDistribution A closed-form scalar distribution that is also smooth.
interface	UnivariateDistribution<NumberType extends java.lang.Number> A Distribution that takes Doubles as inputs and can compute its variance.
interface	UnivariateProbabilityDensityFunction A PDF that takes doubles as input.

Classes in gov.sandia.cognition.statistics that implement Distribution

Modifier and Type	Class and Description
class	AbstractClosedFormIntegerDistribution An abstract class for closed-form integer distributions.
class	AbstractClosedFormSmoothUnivariateDistribution Partial implementation of SmoothUnivariateDistribution
class	AbstractClosedFormUnivariateDistribution<NumberType extends java.lang.Number> Partial implementation of a ClosedFormUnivariateDistribution.
class	AbstractDataDistribution<KeyType> An abstract implementation of the DataDistribution interface.
class	AbstractDistribution<DataType> Partial implementation of Distribution.
class	AbstractRandomVariable<DataType> Partial implementation of RandomVariable.
class	UnivariateRandomVariable This is an implementation of a RandomVariable for scalar distributions.

Methods in gov.sandia.cognition.statistics with parameters of type Distribution

Modifier and Type	Method and Description
protected void	DefaultDistributionParameter.assignParameterMethods(Distribution<?> conditionalDistribution, java.lang.String parameterName) Assigns the getter and setter from the given conditionalDistribution and parameter name.

Uses of Distribution in gov.sandia.cognition.statistics.bayesian

Classes in gov.sandia.cognition.statistics.bayesian with type parameters of type Distribution

Modifier and Type	Class and Description
class	AbstractBayesianParameter<ParameterType,ConditionalType extends ClosedFormDistribution<?>,PriorType extends Distribution<ParameterType>> Partial implementation of BayesianParameter
interface	BayesianEstimator<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>> A type of estimation procedure based on Bayes's rule, which allows us to estimate the uncertainty of parameters given a set of observations that we are given.
interface	BayesianEstimatorPredictor<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>> A BayesianEstimator that can also compute the predictive distribution of new data given the posterior.
interface	BayesianParameter<ParameterType,ConditionalType extends Distribution<?>,PriorType extends Distribution<ParameterType>> A parameter from a Distribution that has an assumed Distribution of values.
interface	BayesianParameter<ParameterType,ConditionalType extends Distribution<?>,PriorType extends Distribution<ParameterType>> A parameter from a Distribution that has an assumed Distribution of values.
interface	BayesianRegression<OutputType,PosteriorType extends Distribution<? extends Vector>> A type of regression algorithm maps a Vector space, and the weights of this Vector space are represented as a posterior distribution given the observed InputOutputPairs.
class	DefaultBayesianParameter<ParameterType,ConditionalType extends ClosedFormDistribution<?>,PriorType extends Distribution<ParameterType>> Default implementation of BayesianParameter using reflection.
interface	RecursiveBayesianEstimator<ObservationType,ParameterType,BeliefType extends Distribution<ParameterType>> A recursive Bayesian estimator is an estimation method that uses the previous belief of the system parameter and a single observation to refine the estimate of the system parameter.

Classes in gov.sandia.cognition.statistics.bayesian that implement Distribution

Modifier and Type	Class and Description
class	AdaptiveRejectionSampling.UpperEnvelope Constructs the upper envelope for sampling.

Methods in gov.sandia.cognition.statistics.bayesian with type parameters of type Distribution

Modifier and Type	Method and Description
static <ParameterType,ConditionalType extends ClosedFormDistribution<?>,PriorType extends Distribution<ParameterType>> DefaultBayesianParameter<ParameterType,ConditionalType,PriorType>	DefaultBayesianParameter.create(ConditionalType conditionalDistribution, java.lang.String parameterName, PriorType parameterPrior) Creates a new instance of DefaultBayesianParameter

Methods in gov.sandia.cognition.statistics.bayesian that return Distribution

Modifier and Type	Method and Description
Distribution<OutputType>	BayesianRegression.createConditionalDistribution(Vectorizable input, Vector weights) Creates the distribution from which the outputs are generated, given the weights and the input to consider.

Methods in gov.sandia.cognition.statistics.bayesian with parameters of type Distribution

Modifier and Type	Method and Description
static <ObservationType,ParameterType> java.util.ArrayList<? extends ObservationType>	BayesianUtil.sample(ClosedFormDistribution<ObservationType> conditional, java.lang.String parameterName, Distribution<ParameterType> prior, java.util.Random random, int numSamples) Samples from the given BayesianParameter by first sampling the prior distribution, then updating the conditional distribution, then sampling from the updated conditional distribution.

Method parameters in gov.sandia.cognition.statistics.bayesian with type arguments of type Distribution

Modifier and Type	Method and Description
static <ObservationType,ParameterType> java.util.ArrayList<ObservationType>	BayesianUtil.sample(BayesianParameter<ParameterType,? extends Distribution<ObservationType>,? extends Distribution<ParameterType>> parameter, java.util.Random random, int numSamples) Samples from the given BayesianParameter by first sampling the prior distribution, then updating the conditional distribution, then sampling from the updated conditional distribution.
static <ObservationType,ParameterType> java.util.ArrayList<ObservationType>	BayesianUtil.sample(BayesianParameter<ParameterType,? extends Distribution<ObservationType>,? extends Distribution<ParameterType>> parameter, java.util.Random random, int numSamples) Samples from the given BayesianParameter by first sampling the prior distribution, then updating the conditional distribution, then sampling from the updated conditional distribution.

Uses of Distribution in gov.sandia.cognition.statistics.distribution

Classes in gov.sandia.cognition.statistics.distribution with type parameters of type Distribution

Modifier and Type	Class and Description
class	LinearMixtureModel<DataType,DistributionType extends Distribution<DataType>> A linear mixture of RandomVariables, with a prior probability distribution.

Classes in gov.sandia.cognition.statistics.distribution that implement Distribution

Modifier and Type	Class and Description
class	BernoulliDistribution A Bernoulli distribution, which takes a value of "1" with probability "p" and value of "0" with probability "1-p".
static class	BernoulliDistribution.CDF CDF of a Bernoulli distribution.
static class	BernoulliDistribution.PMF PMF of the Bernoulli distribution.
class	BetaBinomialDistribution A Binomial distribution where the binomial parameter, p, is set according to a Beta distribution instead of a single value.
static class	BetaBinomialDistribution.CDF CDF of BetaBinomialDistribution
static class	BetaBinomialDistribution.PMF PMF of the BetaBinomialDistribution
class	BetaDistribution Computes the Beta-family of probability distributions.
static class	BetaDistribution.CDF CDF of the Beta-family distribution
static class	BetaDistribution.PDF Beta distribution probability density function
class	BinomialDistribution Binomial distribution, which is a collection of Bernoulli trials
static class	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"
static class	BinomialDistribution.PMF The Probability Mass Function of a binomial distribution.
class	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.
static class	CategoricalDistribution.PMF PMF of the Categorical Distribution
class	CauchyDistribution A Cauchy Distribution is the ratio of two Gaussian Distributions, sometimes known as the Lorentz distribution.
static class	CauchyDistribution.CDF CDF of the CauchyDistribution.
static class	CauchyDistribution.PDF PDF of the CauchyDistribution.
class	ChineseRestaurantProcess A Chinese Restaurant Process is a discrete stochastic processes that partitions data points to clusters.
static class	ChineseRestaurantProcess.PMF PMF of the Chinese Restaurant Process
class	ChiSquareDistribution Describes a Chi-Square Distribution.
static class	ChiSquareDistribution.CDF Cumulative Distribution Function (CDF) of a Chi-Square Distribution
static class	ChiSquareDistribution.PDF PDF of the Chi-Square distribution
class	DataCountTreeSetBinnedMapHistogram<ValueType extends java.lang.Comparable<? super ValueType>> The DataCountTreeSetBinnedMapHistogram class extends a DefaultDataDistribution by mapping values to user defined bins using a TreeSetBinner.
class	DefaultDataDistribution<KeyType> A default implementation of ScalarDataDistribution that uses a backing map.
static class	DefaultDataDistribution.PMF<KeyType> PMF of the DefaultDataDistribution
class	DeterministicDistribution A deterministic distribution that returns samples at a single point.
static class	DeterministicDistribution.CDF CDF of the deterministic distribution.
static class	DeterministicDistribution.PMF PMF of the deterministic distribution.
class	DirichletDistribution The Dirichlet distribution is the multivariate generalization of the beta distribution.
static class	DirichletDistribution.PDF PDF of the Dirichlet distribution.
class	ExponentialDistribution An Exponential distribution describes the time between events in a poisson process, resulting in a memoryless distribution.
static class	ExponentialDistribution.CDF CDF of the ExponentialDistribution.
static class	ExponentialDistribution.PDF PDF of the ExponentialDistribution.
class	GammaDistribution Class representing the Gamma distribution.
static class	GammaDistribution.CDF CDF of the Gamma distribution
static class	GammaDistribution.PDF Closed-form PDF of the Gamma distribution
class	GeometricDistribution The geometric distribution models the number of successes before the first failure occurs under an independent succession of Bernoulli tests.
static class	GeometricDistribution.CDF CDF of the Geometric distribution
static class	GeometricDistribution.PMF PMF of the Geometric distribution
class	InverseGammaDistribution Defines an inverse-gamma distribution.
static class	InverseGammaDistribution.CDF CDF of the inverseRootFinder-gamma distribution.
static class	InverseGammaDistribution.PDF PDF of the inverseRootFinder-Gamma distribution.
class	InverseWishartDistribution The Inverse-Wishart distribution is the multivariate generalization of the inverse-gamma distribution.
static class	InverseWishartDistribution.PDF PDF of the Inverse-Wishart distribution, though I have absolutely no idea why anybody would evaluate the PDF of an Inverse-Wishart...
class	KolmogorovDistribution Contains the Cumulative Distribution Function description for the "D" statistic used within the Kolmogorov-Smirnov test.
static class	KolmogorovDistribution.CDF Contains the Cumulative Distribution Function description for the "D" statistic used within the Kolmogorov-Smirnov test.
class	LaplaceDistribution A Laplace distribution, sometimes called a double exponential distribution.
static class	LaplaceDistribution.CDF CDF of the Laplace distribution.
static class	LaplaceDistribution.PDF The PDF of a Laplace Distribution.
class	LinearMixtureModel<DataType,DistributionType extends Distribution<DataType>> A linear mixture of RandomVariables, with a prior probability distribution.
class	LogisticDistribution A implementation of the scalar logistic distribution, which measures the log-odds of a binary event.
static class	LogisticDistribution.CDF CDF of the LogisticDistribution
static class	LogisticDistribution.PDF PDF of the LogisticDistribution
class	LogNormalDistribution Log-Normal distribution PDF and CDF implementations.
static class	LogNormalDistribution.CDF CDF of the Log-Normal Distribution
static class	LogNormalDistribution.PDF PDF of a Log-normal distribution
static class	MixtureOfGaussians.PDF PDF of the MixtureOfGaussians
class	MultinomialDistribution A multinomial distribution is the multivariate/multiclass generalization of the Binomial distribution.
static class	MultinomialDistribution.PMF Probability Mass Function of the Multinomial Distribution.
class	MultivariateGaussian The MultivariateGaussian class implements a multidimensional Gaussian distribution that contains a mean vector and a covariance matrix.
static class	MultivariateGaussian.PDF PDF of a multivariate Gaussian
class	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.
class	MultivariateMixtureDensityModel<DistributionType extends ClosedFormComputableDistribution<Vector>> A LinearMixtureModel of multivariate distributions with associated PDFs.
static class	MultivariateMixtureDensityModel.PDF<DistributionType extends ClosedFormComputableDistribution<Vector>> PDF of the MultivariateMixtureDensityModel
class	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.
static class	MultivariatePolyaDistribution.PMF PMF of the MultivariatePolyaDistribution
class	MultivariateStudentTDistribution Multivariate generalization of the noncentral Student's t-distribution.
static class	MultivariateStudentTDistribution.PDF PDF of the MultivariateStudentTDistribution
class	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.
static class	NegativeBinomialDistribution.CDF CDF of the NegativeBinomialDistribution
static class	NegativeBinomialDistribution.PMF PMF of the NegativeBinomialDistribution.
class	NormalInverseGammaDistribution The normal inverse-gamma distribution is the product of a univariate Gaussian distribution with an inverse-gamma distribution.
static class	NormalInverseGammaDistribution.PDF PDF of the NormalInverseGammaDistribution
class	NormalInverseWishartDistribution The normal inverse Wishart distribution
static class	NormalInverseWishartDistribution.PDF PDF of the normal inverse-Wishart distribution.
class	ParetoDistribution This class describes the Pareto distribution, sometimes called the Bradford Distribution.
static class	ParetoDistribution.CDF CDF of the Pareto Distribution.
static class	ParetoDistribution.PDF PDF of the ParetoDistribution
class	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.
static class	PoissonDistribution.CDF CDF of the PoissonDistribution
static class	PoissonDistribution.PMF PMF of the PoissonDistribution.
class	ScalarDataDistribution A Data Distribution that uses Doubles as its keys, making it a univariate distribution
static class	ScalarDataDistribution.CDF CDF of the ScalarDataDistribution, maintains the keys/domain in sorted order (TreeMap), so it's slower than it's peers.
static class	ScalarDataDistribution.PMF PMF of the ScalarDataDistribution
class	ScalarMixtureDensityModel ScalarMixtureDensityModel (SMDM) implements just that: a scalar mixture density model.
static class	ScalarMixtureDensityModel.CDF CDFof the SMDM
static class	ScalarMixtureDensityModel.PDF PDF of the SMDM
class	SnedecorFDistribution CDF of the Snedecor F-distribution (also known as Fisher F-distribution, Fisher-Snedecor F-distribution, or just plain old F-distribution).
static class	SnedecorFDistribution.CDF CDF of the F-distribution.
class	StudentizedRangeDistribution Implementation of the Studentized Range distribution, which defines the population correction factor when performing multiple comparisons.
static class	StudentizedRangeDistribution.CDF CDF of the StudentizedRangeDistribution
class	StudentTDistribution Defines a noncentral Student-t Distribution.
static class	StudentTDistribution.CDF Evaluator that computes the Cumulative Distribution Function (CDF) of a Student-t distribution with a fixed number of degrees of freedom
static class	StudentTDistribution.PDF Evaluator that computes the Probability Density Function (CDF) of a Student-t distribution with a fixed number of degrees of freedom
class	UniformDistribution Contains the (very simple) definition of a continuous Uniform distribution, parameterized between the minimum and maximum bounds.
static class	UniformDistribution.CDF Cumulative Distribution Function of a uniform
static class	UniformDistribution.PDF Probability density function of a Uniform Distribution
class	UniformIntegerDistribution Contains the (very simple) definition of a continuous Uniform distribution, parameterized between the minimum and maximum bounds.
static class	UniformIntegerDistribution.CDF Implements the cumulative distribution function for the discrete uniform distribution.
static class	UniformIntegerDistribution.PMF Probability mass function of a discrete uniform distribution.
class	UnivariateGaussian This class contains internal classes that implement useful functions based on the Gaussian distribution.
static class	UnivariateGaussian.CDF CDF of the underlying Gaussian.
static class	UnivariateGaussian.CDF.Inverse Inverts the CumulativeDistribution function.
static class	UnivariateGaussian.PDF PDF of the underlying Gaussian.
class	WeibullDistribution Describes a Weibull distribution, which is often used to describe the mortality, lifespan, or size distribution of objects.
static class	WeibullDistribution.CDF CDF of the Weibull distribution
static class	WeibullDistribution.PDF PDF of the Weibull distribution
class	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).
static class	YuleSimonDistribution.CDF CDF of the Yule-Simon Distribution
static class	YuleSimonDistribution.PMF PMF of the Yule-Simon Distribution

Uses of Distribution in gov.sandia.cognition.statistics.montecarlo

Methods in gov.sandia.cognition.statistics.montecarlo that return Distribution

Modifier and Type	Method and Description
Distribution<? extends OutputType>	MonteCarloIntegrator.getMean(java.util.Collection<? extends OutputType> samples) Computes the Monte Carlo distribution of the given samples.
Distribution<? extends OutputType>	MonteCarloIntegrator.getMean(java.util.List<? extends WeightedValue<? extends OutputType>> samples) Computes the Monte Carlo distribution of the given weighted samples.
<SampleType> Distribution<? extends OutputType>	MonteCarloIntegrator.integrate(java.util.Collection<? extends SampleType> samples, Evaluator<? super SampleType,? extends OutputType> expectationFunction) Integrates the given function given samples from another function.
<SampleType> Distribution<? extends OutputType>	MonteCarloIntegrator.integrate(java.util.List<? extends WeightedValue<? extends SampleType>> samples, Evaluator<? super SampleType,? extends OutputType> expectationFunction) Integrates the given function given weighted samples from another function.

Methods in gov.sandia.cognition.statistics.montecarlo with parameters of type Distribution

Modifier and Type	Method and Description
static UnivariateGaussian	MultivariateCumulativeDistributionFunction.compute(Vector input, Distribution<Vector> distribution, java.util.Random random, double probabilityTolerance) Computes a multi-variate cumulative distribution for a given input according to the given distribution.