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

Packages that use ProbabilityFunction

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.learning.function.cost	Provides cost functions.
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 ProbabilityFunction in gov.sandia.cognition.learning.algorithm.hmm

Fields in gov.sandia.cognition.learning.algorithm.hmm declared as ProbabilityFunction

Modifier and Type	Field and Description
protected ProbabilityFunction<ObservationType>	ParallelHiddenMarkovModel.ObservationLikelihoodTask.distributionFunction The PDF.

Methods in gov.sandia.cognition.learning.algorithm.hmm that return ProbabilityFunction

Modifier and Type	Method and Description
ProbabilityFunction<ObservationType>	ParallelBaumWelchAlgorithm.DistributionEstimatorTask.call()

Methods in gov.sandia.cognition.learning.algorithm.hmm that return types with arguments of type ProbabilityFunction

Modifier and Type	Method and Description
protected java.util.ArrayList<ProbabilityFunction<ObservationType>>	BaumWelchAlgorithm.updateProbabilityFunctions(java.util.ArrayList<Vector> sequenceGammas) Updates the probability function from the concatenated gammas from all sequences
protected java.util.ArrayList<ProbabilityFunction<ObservationType>>	ParallelBaumWelchAlgorithm.updateProbabilityFunctions(java.util.ArrayList<Vector> sequenceGammas)

Method parameters in gov.sandia.cognition.learning.algorithm.hmm with type arguments of type ProbabilityFunction

Modifier and Type	Method and Description
static <ObservationType> HiddenMarkovModel<ObservationType>	HiddenMarkovModel.createRandom(java.util.Collection<? extends ProbabilityFunction<ObservationType>> distributions, java.util.Random random) Creates a Hidden Markov Model with the given probability function for each state, but sampling the columns of the transition matrix and the initial probability distributions from a diffuse Dirichlet.

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

Methods in gov.sandia.cognition.learning.function.categorization that return types with arguments of type ProbabilityFunction

Modifier and Type	Method and Description
WeightedValue<ProbabilityFunction<ObservationType>>	MaximumAPosterioriCategorizer.getCategory(CategoryType category) Gets the prior probability weight and conditional distribution for the given category.

Methods in gov.sandia.cognition.learning.function.categorization with parameters of type ProbabilityFunction

Modifier and Type	Method and Description
void	MaximumAPosterioriCategorizer.addCategory(CategoryType category, double mass, ProbabilityFunction<ObservationType> conditional) Adds the given category with the given mass (which is divided by the masses of all categories to determine the prior probability weight) and the distribution function

Uses of ProbabilityFunction in gov.sandia.cognition.learning.function.cost

Fields in gov.sandia.cognition.learning.function.cost declared as ProbabilityFunction

Modifier and Type	Field and Description
protected ProbabilityFunction<DataType>	ParallelNegativeLogLikelihood.NegativeLogLikelihoodTask.probabilityFunction Probability function to compute the log likelihood

Methods in gov.sandia.cognition.learning.function.cost with parameters of type ProbabilityFunction

Modifier and Type	Method and Description
static <DataType> double	NegativeLogLikelihood.evaluate(ProbabilityFunction<DataType> f, java.util.Collection<? extends DataType> data) Evaluates the negative log-likelihood of the given collection of data according to the given probability function.

Uses of ProbabilityFunction in gov.sandia.cognition.statistics

Subinterfaces of ProbabilityFunction in gov.sandia.cognition.statistics

Modifier and Type	Interface and Description
static interface	DataDistribution.PMF<KeyType> Interface for the probability mass function (PMF) of a data distribution.
interface	ProbabilityDensityFunction<DataType> Defines a probability density function.
interface	ProbabilityMassFunction<DataType> The ProbabilityMassFunction interface defines the functionality of a probability mass function.
interface	UnivariateProbabilityDensityFunction A PDF that takes doubles as input.

Methods in gov.sandia.cognition.statistics that return ProbabilityFunction

Modifier and Type	Method and Description
ProbabilityFunction<DomainType>	ComputableDistribution.getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.

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

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

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

Fields in gov.sandia.cognition.statistics.bayesian declared as ProbabilityFunction

Modifier and Type	Field and Description
protected ProbabilityFunction<ObservationType>	DirichletProcessMixtureModel.conditionalPriorPredictive Base predictive distribution that determines the value of the new cluster weighting during the Gibbs sampling.
protected ProbabilityFunction<java.lang.Double>	RejectionSampling.ScalarEstimator.MinimizerFunction.sampler Sampler function

Fields in gov.sandia.cognition.statistics.bayesian with type parameters of type ProbabilityFunction

Modifier and Type	Field and Description
protected BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>>	ImportanceSampling.DefaultUpdater.conjuctive Defines the parameter that connects the conditional and prior distributions.
protected BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>>	ImportanceSampling.DefaultUpdater.conjuctive Defines the parameter that connects the conditional and prior distributions.
protected BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>>	RejectionSampling.DefaultUpdater.conjuctive Defines the parameter that connects the conditional and prior distributions.
protected BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>>	RejectionSampling.DefaultUpdater.conjuctive Defines the parameter that connects the conditional and prior distributions.

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

Modifier and Type	Method and Description
ProbabilityFunction<ObservationType>	DirichletProcessMixtureModel.Updater.createClusterPosterior(java.lang.Iterable<? extends ObservationType> values, java.util.Random random) Updates the cluster from the values assigned to it
ProbabilityFunction<ObservationType>	DirichletProcessMixtureModel.Updater.createPriorPredictive(java.lang.Iterable<? extends ObservationType> data) Creates the prior predictive distribution from the data.
ProbabilityFunction<? super ObservationType>	DirichletProcessMixtureModel.DPMMCluster.getProbabilityFunction() Getter for probabilityFunction
ProbabilityFunction<ParameterType>	RejectionSampling.DefaultUpdater.getSampler() Getter for sampler

Methods in gov.sandia.cognition.statistics.bayesian that return types with arguments of type ProbabilityFunction

Modifier and Type	Method and Description
BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>>	ImportanceSampling.DefaultUpdater.getConjuctive() Getter for conjunctive
BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>>	ImportanceSampling.DefaultUpdater.getConjuctive() Getter for conjunctive
BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>>	RejectionSampling.DefaultUpdater.getConjuctive() Getter for conjunctive
BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>>	RejectionSampling.DefaultUpdater.getConjuctive() Getter for conjunctive

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

Modifier and Type	Method and Description
void	DirichletProcessMixtureModel.DPMMCluster.setProbabilityFunction(ProbabilityFunction<? super ObservationType> probabilityFunction) Setter for probabilityFunction
void	RejectionSampling.DefaultUpdater.setSampler(ProbabilityFunction<ParameterType> sampler) Setter for sampler

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

Modifier and Type	Method and Description
void	ImportanceSampling.DefaultUpdater.setConjuctive(BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>> conjuctive) Setter for conjunctive
void	ImportanceSampling.DefaultUpdater.setConjuctive(BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>> conjuctive) Setter for conjunctive
void	RejectionSampling.DefaultUpdater.setConjuctive(BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>> conjuctive) Setter for conjunctive
void	RejectionSampling.DefaultUpdater.setConjuctive(BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>> conjuctive) Setter for conjunctive

Constructors in gov.sandia.cognition.statistics.bayesian with parameters of type ProbabilityFunction

Constructor and Description
DefaultUpdater(BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>> conjuctive, java.lang.Double scale, ProbabilityFunction<ParameterType> sampler) Creates a new instance of DefaultUpdater
DefaultUpdater(BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>> conjuctive, ProbabilityFunction<ParameterType> sampler) Creates a new instance of DefaultUpdater
DPMMCluster(java.util.Collection<? extends ObservationType> assignedData, ProbabilityFunction<? super ObservationType> probabilityFunction) Creates a new instance of DPMMCluster
MinimizerFunction(ProbabilityFunction<java.lang.Double> sampler) Creates a new instance of MinimizerFunction
PDFLogEvaluator(ProbabilityFunction<java.lang.Double> function) Creates a new instance of PDFLogEvaluator

Constructor parameters in gov.sandia.cognition.statistics.bayesian with type arguments of type ProbabilityFunction

Constructor and Description
DefaultUpdater(BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>> conjuctive) Creates a new instance of DefaultUpdater
DefaultUpdater(BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>> conjuctive) Creates a new instance of DefaultUpdater
DefaultUpdater(BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>> conjuctive) Creates a new instance of DefaultUpdater
DefaultUpdater(BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>> conjuctive) Creates a new instance of DefaultUpdater
DefaultUpdater(BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>> conjuctive, java.lang.Double scale, ProbabilityFunction<ParameterType> sampler) Creates a new instance of DefaultUpdater
DefaultUpdater(BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>> conjuctive, java.lang.Double scale, ProbabilityFunction<ParameterType> sampler) Creates a new instance of DefaultUpdater
DefaultUpdater(BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>> conjuctive, ProbabilityFunction<ParameterType> sampler) Creates a new instance of DefaultUpdater
DefaultUpdater(BayesianParameter<ParameterType,? extends ProbabilityFunction<ObservationType>,? extends ProbabilityFunction<ParameterType>> conjuctive, ProbabilityFunction<ParameterType> sampler) Creates a new instance of DefaultUpdater
ScalarEstimator(BayesianParameter<java.lang.Double,? extends ProbabilityFunction<ObservationType>,? extends UnivariateProbabilityDensityFunction> conjunctive, java.lang.Iterable<? extends ObservationType> data) Creates a new instance of ScalarEstimator

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

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

Modifier and Type	Class and Description
static class	BernoulliDistribution.PMF PMF of the Bernoulli distribution.
static class	BetaBinomialDistribution.PMF PMF of the BetaBinomialDistribution
static class	BetaDistribution.PDF Beta distribution probability density function
static class	BinomialDistribution.PMF The Probability Mass Function of a binomial distribution.
static class	CategoricalDistribution.PMF PMF of the Categorical Distribution
static class	CauchyDistribution.PDF PDF of the CauchyDistribution.
static class	ChineseRestaurantProcess.PMF PMF of the Chinese Restaurant Process
static class	ChiSquareDistribution.PDF PDF of the Chi-Square distribution
static class	DefaultDataDistribution.PMF<KeyType> PMF of the DefaultDataDistribution
static class	DeterministicDistribution.PMF PMF of the deterministic distribution.
static class	DirichletDistribution.PDF PDF of the Dirichlet distribution.
static class	ExponentialDistribution.PDF PDF of the ExponentialDistribution.
static class	GammaDistribution.PDF Closed-form PDF of the Gamma distribution
static class	GeometricDistribution.PMF PMF of the Geometric distribution
static class	InverseGammaDistribution.PDF PDF of the inverseRootFinder-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...
static class	LaplaceDistribution.PDF The PDF of a Laplace Distribution.
static class	LogisticDistribution.PDF PDF of the LogisticDistribution
static class	LogNormalDistribution.PDF PDF of a Log-normal distribution
static class	MixtureOfGaussians.PDF PDF of the MixtureOfGaussians
static class	MultinomialDistribution.PMF Probability Mass Function of the Multinomial Distribution.
static class	MultivariateGaussian.PDF PDF of a multivariate Gaussian
static class	MultivariateMixtureDensityModel.PDF<DistributionType extends ClosedFormComputableDistribution<Vector>> PDF of the MultivariateMixtureDensityModel
static class	MultivariatePolyaDistribution.PMF PMF of the MultivariatePolyaDistribution
static class	MultivariateStudentTDistribution.PDF PDF of the MultivariateStudentTDistribution
static class	NegativeBinomialDistribution.PMF PMF of the NegativeBinomialDistribution.
static class	NormalInverseGammaDistribution.PDF PDF of the NormalInverseGammaDistribution
static class	NormalInverseWishartDistribution.PDF PDF of the normal inverse-Wishart distribution.
static class	ParetoDistribution.PDF PDF of the ParetoDistribution
static class	PoissonDistribution.PMF PMF of the PoissonDistribution.
static class	ScalarDataDistribution.PMF PMF of the ScalarDataDistribution
static class	ScalarMixtureDensityModel.PDF PDF of the SMDM
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
static class	UniformDistribution.PDF Probability density function of a Uniform Distribution
static class	UniformIntegerDistribution.PMF Probability mass function of a discrete uniform distribution.
static class	UnivariateGaussian.PDF PDF of the underlying Gaussian.
static class	WeibullDistribution.PDF PDF of the Weibull distribution
static class	YuleSimonDistribution.PMF PMF of the Yule-Simon Distribution

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

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

Modifier and Type	Method and Description
ProbabilityFunction<DataType>	ImportanceSampler.getImportanceDistribution() Getter for importanceDistribution.

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

Modifier and Type	Method and Description
java.util.ArrayList<? extends DataType>	DirectSampler.sample(ProbabilityFunction<DataType> targetFunction, java.util.Random random, int numSamples)
void	ImportanceSampler.setImportanceDistribution(ProbabilityFunction<DataType> importanceDistribution) Setter for importanceDistribution.