Uses of Interface
de.jstacs.sequenceScores.statisticalModels.differentiable.SamplingDifferentiableStatisticalModel

Packages that use SamplingDifferentiableStatisticalModel

de.jstacs.classifiers.differentiableSequenceScoreBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingDifferentiableStatisticalModels and that sample parameters using the Metropolis-Hastings algorithm.
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.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture	Provides DifferentiableSequenceScores that are mixtures of other DifferentiableSequenceScores.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models	The package provides different implementations of hidden Markov models based on AbstractHMM.

Uses of SamplingDifferentiableStatisticalModel in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Fields in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling declared as SamplingDifferentiableStatisticalModel

protected SamplingDifferentiableStatisticalModel[]

SamplingScoreBasedClassifier.scoringFunctions SamplingDifferentiableStatisticalModels

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type SamplingDifferentiableStatisticalModel

SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet params, BurnInTest burnInTest, double[] classVariances, LogPrior prior, double[] beta, SamplingDifferentiableStatisticalModel... scoringFunctions)
Creates a new SamplingGenDisMixClassifier using the external parameters params, a burn-in test, a set of sampling variances for the different classes, a prior on the parameters, weights beta for the three components of the LogGenDisMixFunction, i.e., likelihood, conditional likelihood, and prior, and scoring functions that model the distribution for each of the classes.

SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet params, BurnInTest burnInTest, double[] classVariances, LogPrior prior, LearningPrinciple principle, SamplingDifferentiableStatisticalModel... scoringFunctions)
Creates a new SamplingGenDisMixClassifier using the external parameters params, a burn-in test, a set of sampling variances for the different classes, a prior on the parameters, a learning principle, and scoring functions that model the distribution for each of the classes.

SamplingScoreBasedClassifier(SamplingScoreBasedClassifierParameterSet params, BurnInTest burnInTest, double[] classVariances, SamplingDifferentiableStatisticalModel... scoringFunctions)
Creates a new SamplingScoreBasedClassifier using the parameters in params, a specified BurnInTest (or null for no burn-in test), a set of sampling variances, which may be different for each of the classes (in analogy to equivalent sample size for the Dirichlet distribution), and set set of SamplingDifferentiableStatisticalModels for each of the classes.

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

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable that implement SamplingDifferentiableStatisticalModel

class	CyclicMarkovModelDiffSM This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
class	MarkovRandomFieldDiffSM This class implements the scoring function for any MRF (Markov Random Field).
class	UniformDiffSM This DifferentiableStatisticalModel does nothing.

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

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that implement SamplingDifferentiableStatisticalModel

class

MarkovModelDiffSM This class implements a AbstractDifferentiableStatisticalModel for an inhomogeneous Markov model.

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

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous that implement SamplingDifferentiableStatisticalModel

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 SamplingDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture that implement SamplingDifferentiableStatisticalModel

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 SamplingDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif that implement SamplingDifferentiableStatisticalModel

class

ExtendedZOOPSDiffSM This class handles mixtures with at least one hidden motif.

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

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models that implement SamplingDifferentiableStatisticalModel

class

DifferentiableHigherOrderHMM This class combines an HigherOrderHMM and a DifferentiableStatisticalModel by implementing some of the declared methods.