Uses of Class
de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel

Packages that use AbstractTrainableStatisticalModel

de.jstacs.sequenceScores.statisticalModels.trainable	Provides all TrainableStatisticalModels, which can be learned from a single DataSet.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous	This package contains various inhomogeneous models.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared
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
de.jstacs.sequenceScores.statisticalModels.trainable.mixture	This package is the super package for any mixture model.
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable

class	CompositeTrainSM This class is for modelling sequences by modelling the different positions of the each sequence by different models.
class	DifferentiableStatisticalModelWrapperTrainSM This model can be used to use a DifferentiableStatisticalModel as model.
class	UniformTrainSM This class represents a uniform model.
class	VariableLengthWrapperTrainSM This class allows to train any TrainableStatisticalModel on DataSets of Sequences with variable length if each individual length is at least SequenceScore.getLength().

Methods in de.jstacs.sequenceScores.statisticalModels.trainable that return AbstractTrainableStatisticalModel

AbstractTrainableStatisticalModel

AbstractTrainableStatisticalModel.clone() Follows the conventions of Object's clone()-method.

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete

class

DiscreteGraphicalTrainSM This is the main class for all discrete graphical models (DGM).

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous

class	HomogeneousMM This class implements homogeneous Markov models (hMM) of arbitrary order.
class	HomogeneousTrainSM This class implements homogeneous models of arbitrary order.

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

class	BayesianNetworkTrainSM The class implements a Bayesian network ( StructureLearner.ModelType.BN ) of fixed order.
class	DAGTrainSM The abstract class for directed acyclic graphical models (DAGTrainSM).
class	FSDAGModelForGibbsSampling This is the class for a fixed structure directed acyclic graphical model (see FSDAGTrainSM) that can be used in a Gibbs sampling.
class	FSDAGTrainSM This class can be used for any discrete fixed structure directed acyclic graphical model ( FSDAGTrainSM).
class	InhomogeneousDGTrainSM This class is the main class for all inhomogeneous discrete graphical models (InhomogeneousDGTrainSM).

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared

class

SharedStructureMixture This class handles a mixture of models with the same structure that is learned via EM.

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

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.hmm

class

AbstractHMM This class is the super class of all implementations hidden Markov models (HMMs) in Jstacs.

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

Subclasses of AbstractTrainableStatisticalModel 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.

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.mixture

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.mixture

class	AbstractMixtureTrainSM This is the abstract class for all kinds of mixture models.
class	MixtureTrainSM The class for a mixture model of any TrainableStatisticalModels.
class	StrandTrainSM This model handles sequences that can either lie on the forward strand or on the reverse complementary strand.

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif

class	HiddenMotifMixture This is the main class that every generative motif discoverer should implement.
class	ZOOPSTrainSM This class enables the user to search for a single motif in a sequence.