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

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

See:
Description

Interface Summary
DifferentiableState | This interface declares a method that allows to evaluate the gradient which is essential for numerical optimization.
SamplingState |
State | This interface declares the methods of any state used in a hidden Markov model.
TrainableState | This class implements method that allows to fill a statistic, which is used to estimate the parameters of a state during, for instance, the Baum-Welch training.

Class Summary
SimpleDifferentiableState | This class implements a `State` based on `Emission` that allows to reuse `Emission`s for different `State`s.
SimpleSamplingState | This class implements a state that can be used for a HMM that obtains its parameters from sampling.
SimpleState | This class implements a `State` based on `Emission` that allows to reuse `Emission`s for different `State`s.

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

The package provides all interfaces and classes for states used in hidden Markov models. Since states can share the same emission, the emissions build a subpackage.

Since:
  Jstacs 1.5
See Also:
  `State`, `de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions`

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