Interface MixtureDistribution
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- 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.
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method 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.
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Method Detail
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getParams
Object[] getParams()
Get the parameters, for each state, of the distribution.- Returns:
- the parameters, for each state, of the distribution
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newDistributions
ProbabilityDistribution[] newDistributions()
Get the distributions (possibly differently parameterized) for all states.- Returns:
- the distributions
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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
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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 observationsu
- this is in eq. (4.13) of Zucchini and MacDonald (2009), p. 65- Returns:
- the MLE estimators
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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
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