smile.stat.distribution

Class DiscreteExponentialFamilyMixture

java.lang.Object

smile.stat.distribution.AbstractDistribution

smile.stat.distribution.DiscreteDistribution

smile.stat.distribution.DiscreteMixture

smile.stat.distribution.DiscreteExponentialFamilyMixture

All Implemented Interfaces:

Distribution

public class DiscreteExponentialFamilyMixture
extends DiscreteMixture

The finite mixture of distributions from discrete exponential family. The EM algorithm is provided to learn the mixture model from data.

Author:

Haifeng Li

Nested Class Summary

Nested classes/interfaces inherited from class smile.stat.distribution.DiscreteMixture

DiscreteMixture.Component

Constructor Summary

Constructors

Constructor and Description
DiscreteExponentialFamilyMixture(List<DiscreteMixture.Component> mixture) Constructor.
DiscreteExponentialFamilyMixture(List<DiscreteMixture.Component> mixture, int[] data) Constructor.

Method Summary

Methods inherited from class smile.stat.distribution.DiscreteMixture

bic, cdf, entropy, getComponents, logp, mean, npara, p, quantile, rand, sd, size, toString, var

Methods inherited from class smile.stat.distribution.DiscreteDistribution

likelihood, logLikelihood, logp, p, quantile

Methods inherited from class smile.stat.distribution.AbstractDistribution

inverseTransformSampling, likelihood, logLikelihood, quantile, quantile, rejection

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

DiscreteExponentialFamilyMixture

public DiscreteExponentialFamilyMixture(List<DiscreteMixture.Component> mixture)

Constructor.

Parameters:

mixture - a list of discrete exponential family distributions.

DiscreteExponentialFamilyMixture

public DiscreteExponentialFamilyMixture(List<DiscreteMixture.Component> mixture,
                                        int[] data)

Constructor. The mixture model will be learned from the given data with the EM algorithm.

Parameters:

mixture - the initial guess of mixture. Components may have different distribution form.

data - the training data.