Uses of Class
de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel

Packages that use AbstractDifferentiableStatisticalModel

de.jstacs.sequenceScores.statisticalModels.differentiable	Provides all DifferentiableStatisticalModels, which can compute the gradient with respect to their parameters for a given input Sequence.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels	Provides DifferentiableStatisticalModels that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous	Provides DifferentiableStatisticalModels that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture	Provides DifferentiableSequenceScores that are mixtures of other DifferentiableSequenceScores.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif

Uses of AbstractDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable

Subclasses of AbstractDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable

class	AbstractVariableLengthDiffSM This abstract class implements some methods declared in DifferentiableStatisticalModel based on the declaration of methods in VariableLengthDiffSM.
class	CyclicMarkovModelDiffSM This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
class	MappingDiffSM This class implements a DifferentiableStatisticalModel that works on mapped Sequences.
class	MarkovRandomFieldDiffSM This class implements the scoring function for any MRF (Markov Random Field).
class	NormalizedDiffSM This class makes an unnormalized DifferentiableStatisticalModel to a normalized DifferentiableStatisticalModel.

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable that return AbstractDifferentiableStatisticalModel

AbstractDifferentiableStatisticalModel

AbstractDifferentiableStatisticalModel.clone()

Uses of AbstractDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Subclasses of AbstractDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

class	BayesianNetworkDiffSM This class implements a scoring function that is a moral directed graphical model, i.e. a moral Bayesian network.
class	MarkovModelDiffSM This class implements a AbstractDifferentiableStatisticalModel for an inhomogeneous Markov model.

Uses of AbstractDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous

Subclasses of AbstractDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous

class	HomogeneousDiffSM This is the main class for all homogeneous DifferentiableSequenceScores.
class	HomogeneousMM0DiffSM This scoring function implements a homogeneous Markov model of order zero (hMM(0)) for a fixed sequence length.
class	HomogeneousMMDiffSM This scoring function implements a homogeneous Markov model of arbitrary order for any sequence length.
class	UniformHomogeneousDiffSM This scoring function does nothing.

Uses of AbstractDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

Subclasses of AbstractDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

class	AbstractMixtureDiffSM This main abstract class for any mixture scoring function (e.g.
class	MixtureDiffSM This class implements a real mixture model.
class	StrandDiffSM This class enables the user to search on both strand.
class	VariableLengthMixtureDiffSM This class implements a mixture of VariableLengthDiffSM by extending MixtureDiffSM and implementing the methods of VariableLengthDiffSM.

Uses of AbstractDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif

Subclasses of AbstractDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif

class	DurationDiffSM This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models.
class	ExtendedZOOPSDiffSM This class handles mixtures with at least one hidden motif.
class	MixtureDurationDiffSM This class implements a mixture of DurationDiffSMs.
class	PositionDiffSM This class implements a position scoring function that enables the user to get a score without using a Sequence object.
class	SkewNormalLikeDurationDiffSM This class implements a skew normal like discrete truncated distribution.
class	UniformDurationDiffSM This scoring function implements a uniform distribution for positions.