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

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

See:
Description

Interface Summary
DifferentiableTransition | This class declares methods that allow for optimizing the parameters numerically using the `Optimizer`.
SamplingTransition | This interface declares all method used during a sampling.
TrainableAndDifferentiableTransition | This interface unifies the interfaces `TrainableTransition` and `DifferentiableTransition`.
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.

Class Summary
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.
HigherOrderTransition | This class can be used in any `AbstractHMM` allowing to use gradient based or sampling training algorithm.

## Package de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions Description

The package provides all interfaces and classes for transitions used in hidden Markov models. Each transition is based on a set of `BasicHigherOrderTransition.AbstractTransitionElement`s.

Since:
  Jstacs 1.5
See Also:
  `Transition`, `BasicHigherOrderTransition.AbstractTransitionElement`, `de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements`

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