dev.nm.stat.hmm.mixture.distribution

Interface MixtureDistribution

All Known Implementing Classes:

BetaMixtureDistribution, BinomialMixtureDistribution, ExponentialMixtureDistribution, GammaMixtureDistribution, LogNormalMixtureDistribution, NormalMixtureDistribution, PoissonMixtureDistribution

public interface MixtureDistribution

This is the conditional distribution of the observations in each state (possibly differently parameterized) of a mixture hidden Markov model. An EM algorithm, such as the EM algorihtm can fit the parameters.

Method Summary

Modifier and Type	Method and Description
Object[]	getMStepParams(double[] observations, Vector[] u) Maximize, for each state, the log-likelihood of the distribution with respect to the observations and current estimators.
Object[]	getParams() Get the parameters, for each state, of the distribution.
ProbabilityDistribution[]	newDistributions() Get the distributions (possibly differently parameterized) for all states.
MixtureDistribution	newMixtureDistribution(Object[] lambda) Construct a new distribution from a set of parameters, one set per state.
RandomNumberGenerator[]	newRandomNumberGenerators() Get the random number generators corresponding to the distributions (possibly differently parameterized) for all states.

Method Detail

getParams

Object[] getParams()

Get the parameters, for each state, of the distribution.

Returns:

the parameters, for each state, of the distribution

newDistributions

ProbabilityDistribution[] newDistributions()

Get the distributions (possibly differently parameterized) for all states.

Returns:

the distributions

newRandomNumberGenerators

RandomNumberGenerator[] newRandomNumberGenerators()

Get the random number generators corresponding to the distributions (possibly differently parameterized) for all states.

Returns:

the random number generators corresponding to the distributions

getMStepParams

Object[] getMStepParams(double[] observations,
                        Vector[] u)

Maximize, for each state, the log-likelihood of the distribution with respect to the observations and current estimators.

Parameters:

observations - the observations

u - this is in eq. (4.13) of Zucchini and MacDonald (2009), p. 65

Returns:

the MLE estimators

newMixtureDistribution

MixtureDistribution newMixtureDistribution(Object[] lambda)

Construct a new distribution from a set of parameters, one set per state.

Parameters:

lambda - the parameters of the distribution, one set per state

Returns:

a new HMM distribution