See: Description
Interface | Description |
---|---|
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 | Description |
---|---|
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.
|
HigherOrderTransition |
This class can be used in any
AbstractHMM allowing to use gradient based or sampling training algorithm. |
BasicHigherOrderTransition.AbstractTransitionElement
s.