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## 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.

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