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	FSMEManager This class can be used for any discrete fixed structure maximum entropy model (FSMEM).
class	InhomogeneousDGTrainSM This class is the main class for all inhomogeneous discrete graphical models (InhomogeneousDGTrainSM).
class	MEManager This class is the super class for all maximum entropy models

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.