gov.sandia.cognition.statistics

Interface Distribution<DataType>

Type Parameters:

DataType - Type of data used on the domain of this distribution. For example, a scalar distribution would have DataType of Double, and a multivariate distribution would have a DataType of Vector.

All Superinterfaces:

java.lang.Cloneable, CloneableSerializable, java.io.Serializable

All Known Subinterfaces:

ClosedFormComputableDiscreteDistribution<DataType>, ClosedFormComputableDistribution<DataType>, ClosedFormCumulativeDistributionFunction<DomainType>, ClosedFormDiscreteUnivariateDistribution<DomainType>, ClosedFormDistribution<DataType>, ClosedFormUnivariateDistribution<NumberType>, ComputableDistribution<DomainType>, CumulativeDistributionFunction<NumberType>, DataDistribution<DataType>, DataDistribution.PMF<KeyType>, DiscreteDistribution<DataType>, DistributionWithMean<DataType>, EstimableDistribution<ObservationType,DistributionType>, EstimableWeightedDistribution<ObservationType,DistributionType>, IntegerDistribution, InvertibleCumulativeDistributionFunction<NumberType>, ProbabilityDensityFunction<DataType>, ProbabilityFunction<DataType>, ProbabilityMassFunction<DataType>, RandomVariable<DataType>, SmoothCumulativeDistributionFunction, SmoothUnivariateDistribution, UnivariateDistribution<NumberType>, UnivariateProbabilityDensityFunction

All Known Implementing Classes:

AbstractClosedFormIntegerDistribution, AbstractClosedFormSmoothUnivariateDistribution, AbstractClosedFormUnivariateDistribution, AbstractDataDistribution, AbstractDistribution, AbstractRandomVariable, AdaptiveRejectionSampling.UpperEnvelope, BernoulliDistribution, BernoulliDistribution.CDF, BernoulliDistribution.PMF, BetaBinomialDistribution, BetaBinomialDistribution.CDF, BetaBinomialDistribution.PMF, BetaDistribution, BetaDistribution.CDF, BetaDistribution.PDF, BinomialDistribution, BinomialDistribution.CDF, BinomialDistribution.PMF, CategoricalDistribution, CategoricalDistribution.PMF, CauchyDistribution, CauchyDistribution.CDF, CauchyDistribution.PDF, ChineseRestaurantProcess, ChineseRestaurantProcess.PMF, ChiSquareDistribution, ChiSquareDistribution.CDF, ChiSquareDistribution.PDF, DataCountTreeSetBinnedMapHistogram, DefaultDataDistribution, DefaultDataDistribution.PMF, DeterministicDistribution, DeterministicDistribution.CDF, DeterministicDistribution.PMF, DirichletDistribution, DirichletDistribution.PDF, ExponentialDistribution, ExponentialDistribution.CDF, ExponentialDistribution.PDF, GammaDistribution, GammaDistribution.CDF, GammaDistribution.PDF, GeometricDistribution, GeometricDistribution.CDF, GeometricDistribution.PMF, HiddenMarkovModel, InverseGammaDistribution, InverseGammaDistribution.CDF, InverseGammaDistribution.PDF, InverseWishartDistribution, InverseWishartDistribution.PDF, KolmogorovDistribution, KolmogorovDistribution.CDF, LaplaceDistribution, LaplaceDistribution.CDF, LaplaceDistribution.PDF, LinearMixtureModel, LogisticDistribution, LogisticDistribution.CDF, LogisticDistribution.PDF, LogNormalDistribution, LogNormalDistribution.CDF, LogNormalDistribution.PDF, MaximumAPosterioriCategorizer, MixtureOfGaussians.PDF, MultinomialDistribution, MultinomialDistribution.PMF, MultivariateGaussian, MultivariateGaussian.PDF, MultivariateGaussianInverseGammaDistribution, MultivariateMixtureDensityModel, MultivariateMixtureDensityModel.PDF, MultivariatePolyaDistribution, MultivariatePolyaDistribution.PMF, MultivariateStudentTDistribution, MultivariateStudentTDistribution.PDF, NegativeBinomialDistribution, NegativeBinomialDistribution.CDF, NegativeBinomialDistribution.PMF, NormalInverseGammaDistribution, NormalInverseGammaDistribution.PDF, NormalInverseWishartDistribution, NormalInverseWishartDistribution.PDF, ParallelHiddenMarkovModel, ParetoDistribution, ParetoDistribution.CDF, ParetoDistribution.PDF, PoissonDistribution, PoissonDistribution.CDF, PoissonDistribution.PMF, ScalarDataDistribution, ScalarDataDistribution.CDF, ScalarDataDistribution.PMF, ScalarMixtureDensityModel, ScalarMixtureDensityModel.CDF, ScalarMixtureDensityModel.PDF, SnedecorFDistribution, SnedecorFDistribution.CDF, StudentizedRangeDistribution, StudentizedRangeDistribution.CDF, StudentTDistribution, StudentTDistribution.CDF, StudentTDistribution.PDF, UniformDistribution, UniformDistribution.CDF, UniformDistribution.PDF, UniformIntegerDistribution, UniformIntegerDistribution.CDF, UniformIntegerDistribution.PMF, UnivariateGaussian, UnivariateGaussian.CDF, UnivariateGaussian.CDF.Inverse, UnivariateGaussian.PDF, UnivariateRandomVariable, WeibullDistribution, WeibullDistribution.CDF, WeibullDistribution.PDF, YuleSimonDistribution, YuleSimonDistribution.CDF, YuleSimonDistribution.PMF

public interface Distribution<DataType>
extends CloneableSerializable

Describes a very high-level distribution of data. Basically, this is an object that can be sampled according to its distribution of data.

Since:

3.0

Author:

Kevin R. Dixon

Method Summary

Modifier and Type	Method and Description
DataType	sample(java.util.Random random) Draws a single random sample from the distribution.
java.util.ArrayList<DataType>	sample(java.util.Random random, int numSamples) Draws multiple random samples from the distribution.
void	sampleInto(java.util.Random random, int sampleCount, java.util.Collection<? super DataType> output) Draws multiple random samples from the distribution and puts the result into the given collection.

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Method Detail

sample

DataType sample(java.util.Random random)

Draws a single random sample from the distribution.

Parameters:

random - Random-number generator to use in order to generate random numbers.

Returns:

Sample drawn according to this distribution.

sample

java.util.ArrayList<DataType> sample(java.util.Random random,
                                     int numSamples)

Draws multiple random samples from the distribution. It is generally more efficient to use this multiple-sample method than multiple calls of the single-sample method. (But not always.)

Parameters:

random - Random-number generator to use in order to generate random numbers.

numSamples - Number of samples to draw from the distribution.

Returns:

Samples drawn according to this distribution.

sampleInto

void sampleInto(java.util.Random random,
                int sampleCount,
                java.util.Collection<? super DataType> output)

Draws multiple random samples from the distribution and puts the result into the given collection.

Parameters:

random - Random number generator to use.

sampleCount - The number of samples to draw. Cannot be negative.

output - The collection to add the samples into.