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## Uses of Interface
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.TransitionWithSufficientStatistic

Packages that use TransitionWithSufficientStatistic
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions | The package provides all interfaces and classes for transitions used in hidden Markov models.

Uses of TransitionWithSufficientStatistic in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions

Subinterfaces of TransitionWithSufficientStatistic in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions
`interface` | `SamplingTransition` This interface declares all method used during a sampling.
`interface` | `TrainableAndDifferentiableTransition` This interface unifies the interfaces `TrainableTransition` and `DifferentiableTransition`.
`interface` | `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.

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions that implement TransitionWithSufficientStatistic
`class` | `BasicHigherOrderTransition` This class implements the basic transition that allows to be trained using the viterbi or the Baum-Welch algorithm.
`class` | `HigherOrderTransition` This class can be used in any `AbstractHMM` allowing to use gradient based or sampling training algorithm.

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