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

Packages that use Distribution
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
 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
 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
 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
 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 Number>
          Functionality of a cumulative distribution function that's defined with closed-form parameters.
 interface ClosedFormDiscreteUnivariateDistribution<DomainType extends 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 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 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 InvertibleCumulativeDistributionFunction<NumberType extends 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 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
 class AbstractClosedFormSmoothUnivariateDistribution
          Partial implementation of SmoothUnivariateDistribution
 class AbstractClosedFormUnivariateDistribution<NumberType extends 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
protected  void DefaultDistributionParameter.assignParameterMethods(Distribution<?> conditionalDistribution, 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
 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
 class AdaptiveRejectionSampling.UpperEnvelope
          Constructs the upper envelope for sampling.
 

Methods in gov.sandia.cognition.statistics.bayesian with type parameters of type Distribution
static
<ParameterType,ConditionalType extends ClosedFormDistribution<?>,PriorType extends Distribution<ParameterType>>
DefaultBayesianParameter<ParameterType,ConditionalType,PriorType>
DefaultBayesianParameter.create(ConditionalType conditionalDistribution, String parameterName, PriorType parameterPrior)
          Creates a new instance of DefaultBayesianParameter
 

Methods in gov.sandia.cognition.statistics.bayesian that return Distribution
 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
static
<ObservationType,ParameterType>
ArrayList<? extends ObservationType>
BayesianUtil.sample(ClosedFormDistribution<ObservationType> conditional, String parameterName, Distribution<ParameterType> prior, 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
static
<ObservationType,ParameterType>
ArrayList<ObservationType>
BayesianUtil.sample(BayesianParameter<ParameterType,? extends Distribution<ObservationType>,? extends Distribution<ParameterType>> parameter, 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>
ArrayList<ObservationType>
BayesianUtil.sample(BayesianParameter<ParameterType,? extends Distribution<ObservationType>,? extends Distribution<ParameterType>> parameter, 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
 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
 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 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 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
 Distribution<? extends OutputType> MonteCarloIntegrator.getMean(Collection<? extends OutputType> samples)
          Computes the Monte Carlo distribution of the given samples.
 Distribution<? extends OutputType> MonteCarloIntegrator.getMean(List<? extends WeightedValue<? extends OutputType>> samples)
          Computes the Monte Carlo distribution of the given weighted samples.
<SampleType>
Distribution<? extends OutputType>
MonteCarloIntegrator.integrate(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(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
static UnivariateGaussian MultivariateCumulativeDistributionFunction.compute(Vector input, Distribution<Vector> distribution, Random random, double probabilityTolerance)
          Computes a multi-variate cumulative distribution for a given input according to the given distribution.