de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions

Interface TrainableTransition

All Superinterfaces:

Cloneable, Storable, Transition, TransitionWithSufficientStatistic

All Known Subinterfaces:

TrainableAndDifferentiableTransition

All Known Implementing Classes:

BasicHigherOrderTransition, HigherOrderTransition

public interface TrainableTransition
extends TransitionWithSufficientStatistic

This class declares methods that allow for estimating the parameters from a sufficient statistic, as for instance done in the (modified) Baum-Welch algorithm, viterbi training, or Gibbs sampling.

Author:

Jens Keilwagen

Method Summary

Modifier and Type	Method and Description
void	estimateFromStatistic() This method estimates the parameter of the transition using the internal sufficient statistic.

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.TransitionWithSufficientStatistic

addToStatistic, getLogGammaScoreFromStatistic, joinStatistics, resetStatistic

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.Transition

clone, fillTransitionInformation, getChildIdx, getGraphizNetworkRepresentation, getLastContextState, getLogPriorTerm, getLogScoreFor, getMaximalInDegree, getMaximalMarkovOrder, getMaximalNumberOfChildren, getNumberOfChildren, getNumberOfIndexes, getNumberOfStates, hasAnySelfTransitions, initializeRandomly, isAbsoring, setParameters, toString

Methods inherited from interface de.jstacs.Storable

toXML

Method Detail

estimateFromStatistic

void estimateFromStatistic()

This method estimates the parameter of the transition using the internal sufficient statistic.