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 |
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.Transition |
clone, fillTransitionInformation, getChildIdx, getGraphizNetworkRepresentation, getLastContextState, getLogPriorTerm, getLogScoreFor, getMaximalInDegree, getMaximalMarkovOrder, getMaximalNumberOfChildren, getNumberOfChildren, getNumberOfIndexes, getNumberOfStates, hasAnySelfTransitions, initializeRandomly, isAbsoring, setParameters, toString |
estimateFromStatistic
void estimateFromStatistic()
- This method estimates the parameter of the transition using the internal sufficient statistic.