Uses of Package
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions

Packages that use de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions

Package	Description
de.jstacs.sequenceScores.statisticalModels.trainable.hmm	The package provides all interfaces and classes for a hidden Markov model (HMM).
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models	The package provides different implementations of hidden Markov models based on AbstractHMM.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions	The package provides all interfaces and classes for transitions used in hidden Markov models.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions used by de.jstacs.sequenceScores.statisticalModels.trainable.hmm

Class and Description
BasicHigherOrderTransition.AbstractTransitionElement This class declares the probability distribution for a given context, i.e.
Transition This interface declares the methods of the transition used in a hidden Markov model.

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions used by de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

Class and Description
BasicHigherOrderTransition.AbstractTransitionElement This class declares the probability distribution for a given context, i.e.

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions used by de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions

Class and Description
BasicHigherOrderTransition This class implements the basic transition that allows to be trained using the viterbi or the Baum-Welch algorithm.
BasicHigherOrderTransition.AbstractTransitionElement This class declares the probability distribution for a given context, i.e.
DifferentiableTransition This class declares methods that allow for optimizing the parameters numerically using the Optimizer.
HigherOrderTransition This class can be used in any AbstractHMM allowing to use gradient based or sampling training algorithm.
SamplingTransition This interface declares all method used during a sampling.
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.
Transition This interface declares the methods of the transition used in a hidden Markov model.
TransitionWithSufficientStatistic This interface defines method for reseting and filling an internal sufficient statistic.

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions used by de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements

Class and Description
BasicHigherOrderTransition.AbstractTransitionElement This class declares the probability distribution for a given context, i.e.