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

Packages that use Transition
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.transitions | The package provides all interfaces and classes for transitions used in hidden Markov models.

Uses of Transition in de.jstacs.sequenceScores.statisticalModels.trainable.hmm

Fields in de.jstacs.sequenceScores.statisticalModels.trainable.hmm declared as Transition
`protected Transition` | `AbstractHMM.transition` The transitions between all (hidden) states of the HMM.

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

Subinterfaces of Transition in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions
`interface` | `DifferentiableTransition` This class declares methods that allow for optimizing the parameters numerically using the `Optimizer`.
`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.
`interface` | `TransitionWithSufficientStatistic` This interface defines method for reseting and filling an internal sufficient statistic.

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions that implement Transition
`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.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions that return Transition
`Transition` | `Transition.clone()` This method returns a deep clone of the current instance.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions with parameters of type Transition
`void` | `TransitionWithSufficientStatistic.joinStatistics(Transition... transitions)` This method joins the statistics of different instances and sets this joined statistic as statistic of each instance.
`void` | `BasicHigherOrderTransition.joinStatistics(Transition... transitions)`
`void` | `Transition.setParameters(Transition t)` Set values of parameters of the instance to the value of the parameters of the given instance.
`void` | `BasicHigherOrderTransition.setParameters(Transition t)`

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