Package gov.sandia.cognition.statistics

Provides the inheritance hierarchy for general statistical methods and distributions.

See:
          Description

Interface Summary
ClosedFormComputableDiscreteDistribution<DataType> A discrete, closed-form Distribution with a PMF.
ClosedFormComputableDistribution<DataType> A closed-form Distribution that also has an associated distribution function.
ClosedFormCumulativeDistributionFunction<DomainType extends Number> Functionality of a cumulative distribution function that's defined with closed-form parameters.
ClosedFormDiscreteUnivariateDistribution<DomainType extends Number> A ClosedFormUnivariateDistribution that is also a DiscreteDistribution
ClosedFormDistribution<DataType> Defines a distribution that is described a parameterized mathematical equation.
ClosedFormUnivariateDistribution<NumberType extends Number> Defines the functionality associated with a closed-form scalar distribution.
ComputableDistribution<DomainType> A type of Distribution that has an associated distribution function, either a PDF or PMF.
CumulativeDistributionFunction<NumberType extends Number> Functionality of a cumulative distribution function.
DataDistribution<DataType> A distribution of data from which we can sample and perform Ring operations.
DataDistribution.PMF<KeyType> Interface for the probability mass function (PMF) of a data distribution.
DiscreteDistribution<DataType> A Distribution with a countable domain (input) set.
Distribution<DataType> Describes a very high-level distribution of data.
DistributionEstimator<ObservationType,DistributionType extends Distribution<? extends ObservationType>> A BatchLearner that estimates a Distribution.
DistributionParameter<ParameterType,ConditionalType extends Distribution<?>> Allows access to a parameter within a closed-form distribution, given by the high-level String value.
DistributionWeightedEstimator<ObservationType,DistributionType extends Distribution<? extends ObservationType>> A BatchLearner that estimates a Distribution from a Collection of weighted data.
DistributionWithMean<DataType> A Distribution that has a well-defined mean, or first central moment.
EstimableDistribution<ObservationType,DistributionType extends EstimableDistribution<ObservationType,? extends DistributionType>> A Distribution that has an estimator associated with it, typically a closed-form estimator.
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.
InvertibleCumulativeDistributionFunction<NumberType extends Number> A cumulative distribution function that is empirically invertible.
ProbabilityDensityFunction<DataType> Defines a probability density function.
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).
ProbabilityMassFunction<DataType> The ProbabilityMassFunction interface defines the functionality of a probability mass function.
RandomVariable<DataType> Describes the functionality of a random variable.
SmoothCumulativeDistributionFunction This defines a CDF that has an associated derivative, which is its PDF.
SmoothUnivariateDistribution A closed-form scalar distribution that is also smooth.
SufficientStatistic<DataType,DistributionType> Sufficient statistics are the data which are sufficient to store all information to create an underlying parameter, such as a Distribution.
UnivariateDistribution<NumberType extends Number> A Distribution that takes Doubles as inputs and can compute its variance.
UnivariateProbabilityDensityFunction A PDF that takes doubles as input.
 

Class Summary
AbstractClosedFormSmoothUnivariateDistribution Partial implementation of SmoothUnivariateDistribution
AbstractClosedFormUnivariateDistribution<NumberType extends Number> Partial implementation of a ClosedFormUnivariateDistribution.
AbstractDataDistribution<KeyType> An abstract implementation of the DataDistribution interface.
AbstractDistribution<DataType> Partial implementation of Distribution.
AbstractIncrementalEstimator<DataType,DistributionType extends Distribution<? extends DataType>,SufficientStatisticsType extends SufficientStatistic<DataType,DistributionType>> Partial implementation of IncrementalEstimator.
AbstractRandomVariable<DataType> Partial implementation of RandomVariable.
AbstractSufficientStatistic<DataType,DistributionType> Partial implementation of SufficientStatistic
DefaultDistributionParameter<ParameterType,ConditionalType extends ClosedFormDistribution<?>> Default implementation of DistributionParameter using introspection.
DiscreteSamplingUtil A utility class for sampling.
DistributionParameterUtil Functions to assist in creating DistributionParameters.
ProbabilityMassFunctionUtil Utility methods for helping computations in PMFs.
UnivariateRandomVariable This is an implementation of a RandomVariable for scalar distributions.
 

Package gov.sandia.cognition.statistics Description

Provides the inheritance hierarchy for general statistical methods and distributions.

Since:
2.0
Author:
Justin Basilico