See: Description
| Class | Description |
|---|---|
| AbstractBaumWelchAlgorithm<ObservationType,DataType> |
Partial implementation of the Baum-Welch algorithm.
|
| BaumWelchAlgorithm<ObservationType> |
Implements the Baum-Welch algorithm, also known as the "forward-backward
algorithm", the expectation-maximization algorithm, etc for
Hidden Markov Models (HMMs).
|
| HiddenMarkovModel<ObservationType> |
A discrete-state Hidden Markov Model (HMM) with either continuous
or discrete observations.
|
| MarkovChain |
A Markov chain is a random process that has a finite number of states with
random transition probabilities between states at discrete time steps.
|
| ParallelBaumWelchAlgorithm<ObservationType> |
A Parallelized implementation of some of the methods of the
Baum-Welch Algorithm.
|
| ParallelBaumWelchAlgorithm.DistributionEstimatorTask<ObservationType> |
Re-estimates the PDF from the gammas.
|
| ParallelHiddenMarkovModel<ObservationType> |
A Hidden Markov Model with parallelized processing.
|
| ParallelHiddenMarkovModel.ComputeTransitionsTask |
Calls the computeTransitions method.
|
| ParallelHiddenMarkovModel.NormalizeTransitionTask |
Calls the normalizeTransitionMatrix method.
|
| ParallelHiddenMarkovModel.ObservationLikelihoodTask<ObservationType> |
Calls the computeObservationLikelihoods() method.
|
| ParallelHiddenMarkovModel.StateObservationLikelihoodTask |
Calls the computeStateObservationLikelihood() method.
|