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## Uses of Package
de.jstacs.sequenceScores.statisticalModels.differentiable

Packages that use de.jstacs.sequenceScores.statisticalModels.differentiable
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix | Provides an implementation of a classifier that allows to train the parameters of a set of `DifferentiableStatisticalModel`s by a unified generative-discriminative learning principle
de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior | Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling | Provides the classes for `AbstractScoreBasedClassifier`s that are based on `SamplingDifferentiableStatisticalModel`s and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.sequenceScores.statisticalModels.differentiable | Provides all `DifferentiableStatisticalModel`s, which can compute the gradient with respect to their parameters for a given input `Sequence`.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels | Provides `DifferentiableStatisticalModel`s that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous | Provides `DifferentiableStatisticalModel`s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture | Provides `DifferentiableSequenceScore`s that are mixtures of other `DifferentiableSequenceScore`s.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif |
de.jstacs.sequenceScores.statisticalModels.trainable | Provides all `TrainableStatisticalModel`s, which can be learned from a single `DataSet`.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models | The package provides different implementations of hidden Markov models based on `AbstractHMM`

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix
DifferentiableStatisticalModel The interface for normalizable `DifferentiableSequenceScore`s.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior
DifferentiableStatisticalModel The interface for normalizable `DifferentiableSequenceScore`s.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
SamplingDifferentiableStatisticalModel Interface for `DifferentiableStatisticalModel`s that can be used for Metropolis-Hastings sampling in a `SamplingScoreBasedClassifier`.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.differentiable
AbstractDifferentiableStatisticalModel This class is the main part of any `ScoreClassifier`.
AbstractVariableLengthDiffSM This abstract class implements some methods declared in `DifferentiableStatisticalModel` based on the declaration of methods in `VariableLengthDiffSM`.
CyclicMarkovModelDiffSM This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
DifferentiableStatisticalModel The interface for normalizable `DifferentiableSequenceScore`s.
IndependentProductDiffSM This class enables the user to model parts of a sequence independent of each other.
MappingDiffSM This class implements a `DifferentiableStatisticalModel` that works on mapped `Sequence`s.
MarkovRandomFieldDiffSM This class implements the scoring function for any MRF (Markov Random Field).
MultiDimensionalSequenceWrapperDiffSM This class implements a simple wrapper for multidimensional sequences.
NormalizedDiffSM This class makes an unnormalized `DifferentiableStatisticalModel` to a normalized `DifferentiableStatisticalModel`.
SamplingDifferentiableStatisticalModel Interface for `DifferentiableStatisticalModel`s that can be used for Metropolis-Hastings sampling in a `SamplingScoreBasedClassifier`.
VariableLengthDiffSM This is an interface for all `DifferentiableStatisticalModel`s that allow to score subsequences of arbitrary length.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels
AbstractDifferentiableStatisticalModel This class is the main part of any `ScoreClassifier`.
DifferentiableStatisticalModel The interface for normalizable `DifferentiableSequenceScore`s.
SamplingDifferentiableStatisticalModel Interface for `DifferentiableStatisticalModel`s that can be used for Metropolis-Hastings sampling in a `SamplingScoreBasedClassifier`.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous
AbstractDifferentiableStatisticalModel This class is the main part of any `ScoreClassifier`.
AbstractVariableLengthDiffSM This abstract class implements some methods declared in `DifferentiableStatisticalModel` based on the declaration of methods in `VariableLengthDiffSM`.
DifferentiableStatisticalModel The interface for normalizable `DifferentiableSequenceScore`s.
SamplingDifferentiableStatisticalModel Interface for `DifferentiableStatisticalModel`s that can be used for Metropolis-Hastings sampling in a `SamplingScoreBasedClassifier`.
VariableLengthDiffSM This is an interface for all `DifferentiableStatisticalModel`s that allow to score subsequences of arbitrary length.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.differentiable.mixture
AbstractDifferentiableStatisticalModel This class is the main part of any `ScoreClassifier`.
DifferentiableStatisticalModel The interface for normalizable `DifferentiableSequenceScore`s.
SamplingDifferentiableStatisticalModel Interface for `DifferentiableStatisticalModel`s that can be used for Metropolis-Hastings sampling in a `SamplingScoreBasedClassifier`.
VariableLengthDiffSM This is an interface for all `DifferentiableStatisticalModel`s that allow to score subsequences of arbitrary length.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
AbstractDifferentiableStatisticalModel This class is the main part of any `ScoreClassifier`.
DifferentiableStatisticalModel The interface for normalizable `DifferentiableSequenceScore`s.
SamplingDifferentiableStatisticalModel Interface for `DifferentiableStatisticalModel`s that can be used for Metropolis-Hastings sampling in a `SamplingScoreBasedClassifier`.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.trainable
DifferentiableStatisticalModel The interface for normalizable `DifferentiableSequenceScore`s.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable used by de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models
DifferentiableStatisticalModel The interface for normalizable `DifferentiableSequenceScore`s.
SamplingDifferentiableStatisticalModel Interface for `DifferentiableStatisticalModel`s that can be used for Metropolis-Hastings sampling in a `SamplingScoreBasedClassifier`.

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