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## Uses of Class
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM

Packages that use AbstractHMM
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.models | The package provides different implementations of hidden Markov models based on `AbstractHMM`

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

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm that return AbstractHMM
`AbstractHMM` | `AbstractHMM.clone()`
`static AbstractHMM` | `HMMFactory.createErgodicHMM(HMMTrainingParameterSet pars, int order, double ess, double selfTranistionPart, double expectedSequenceLength, Emission... emission)` This method creates an ergodic, i.e. a completely connected, HMM using the given emissions.
`static AbstractHMM` | `HMMFactory.createProfileHMM(MaxHMMTrainingParameterSet trainingParameterSet, double[][] initFromTo, boolean likelihood, int order, int numLayers, AlphabetContainer con, double ess, boolean conditionalMain, boolean closeCircle, double[][] conditionInitProbs, boolean insertUniform)` Creates a new profile HMM for a given architecture and number of layers.
`static AbstractHMM` | `HMMFactory.createProfileHMM(MaxHMMTrainingParameterSet trainingParameterSet, double[][] initFromTo, boolean likelihood, int order, int numLayers, AlphabetContainer con, double ess, boolean conditionalMain, int joiningStates, double[][] conditionInitProbs, boolean insertUniform)` Creates a new profile HMM for a given architecture and number of layers.
`static AbstractHMM` | `HMMFactory.createProfileHMM(MaxHMMTrainingParameterSet trainingParameterSet, HMMFactory.HMMType type, boolean likelihood, int order, int numLayers, AlphabetContainer con, double ess, boolean conditionalMain, boolean closeCircle, double[][] conditionInitProbs)` Creates a new profile HMM for a given architecture and number of layers.
`static AbstractHMM` | `HMMFactory.createPseudoErgodicHMM(HMMTrainingParameterSet pars, double ess, double selfTranistionPart, double finalTranistionPart, AlphabetContainer con, int numStates, boolean insertUniform)` Creates an HMM with `numStates+1` states, where `numStates` emitting build a clique and each of those states is connected to the absorbing silent final state.
`static AbstractHMM` | `HMMFactory.createSunflowerHMM(HMMTrainingParameterSet pars, AlphabetContainer con, double ess, int expectedSequenceLength, boolean startCentral, int... motifLength)` This method creates a first order sunflower HMM.
`static AbstractHMM` | `HMMFactory.createSunflowerHMM(HMMTrainingParameterSet pars, AlphabetContainer con, double ess, int expectedSequenceLength, boolean startCentral, PhyloTree[] t, double[] motifProb, int[] motifLength)` This method creates a first order sunflower HMM allowing phylogenetic emissions.

Uses of AbstractHMM in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

Subclasses of AbstractHMM in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models
`class` | `DifferentiableHigherOrderHMM` This class combines an `HigherOrderHMM` and a `DifferentiableStatisticalModel` by implementing some of the declared methods.
`class` | `HigherOrderHMM` This class implements a higher order hidden Markov model.
`class` | `SamplingHigherOrderHMM`
`class` | `SamplingPhyloHMM` This class implements an (higher order) HMM that contains multi-dimensional emissions described by a phylogenetic tree.

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