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

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

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

BasicHigherOrderTransition.AbstractTransitionElement
This class declares the probability distribution for a given context, i.e. it contains all possible transition and the corresponding probabilities for a given set offset previously visited states.

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

BasicHigherOrderTransition.AbstractTransitionElement
This class declares the probability distribution for a given context, i.e. it contains all possible transition and the corresponding probabilities for a given set offset previously visited states.

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

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. it contains all possible transition and the corresponding probabilities for a given set offset previously visited states.

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

BasicHigherOrderTransition.AbstractTransitionElement
This class declares the probability distribution for a given context, i.e. it contains all possible transition and the corresponding probabilities for a given set offset previously visited states.