Uses of Interface
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.TransitionWithSufficientStatistic

Packages that use TransitionWithSufficientStatistic

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

The package provides all interfaces and classes for transitions used in hidden Markov models.

Uses of TransitionWithSufficientStatistic in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions

Subinterfaces of TransitionWithSufficientStatistic in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions

interface	SamplingTransition This interface declares all method used during a sampling.
interface	TrainableAndDifferentiableTransition This interface unifies the interfaces TrainableTransition and DifferentiableTransition.
interface	TrainableTransition 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.

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions that implement TransitionWithSufficientStatistic

class	BasicHigherOrderTransition This class implements the basic transition that allows to be trained using the viterbi or the Baum-Welch algorithm.
class	HigherOrderTransition This class can be used in any AbstractHMM allowing to use gradient based or sampling training algorithm.