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:
Cloneable, CloneableSerializable, 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>, InvertibleCumulativeDistributionFunction<NumberType>, ProbabilityDensityFunction<DataType>, ProbabilityFunction<DataType>, ProbabilityMassFunction<DataType>, RandomVariable<DataType>, SmoothCumulativeDistributionFunction, SmoothUnivariateDistribution, UnivariateDistribution<NumberType>, UnivariateProbabilityDensityFunction
All Known Implementing Classes:
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, 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
 DataType sample(Random random)
          Draws a single random sample from the distribution.
 ArrayList<? extends DataType> sample(Random random, int numSamples)
          Draws multiple random samples from the distribution.
 
Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable
clone
 

Method Detail

sample

DataType sample(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

ArrayList<? extends DataType> sample(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.