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

Provides all `DifferentiableStatisticalModel`s, which can compute the gradient with respect to their parameters for a given input `Sequence`.

See:
Description

Interface Summary
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.

Class Summary
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.
DifferentiableStatisticalModelFactory | This class allows to easily create some frequently used models.
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`.
UniformDiffSM | This `DifferentiableStatisticalModel` does nothing.

## Package de.jstacs.sequenceScores.statisticalModels.differentiable Description

Provides all `DifferentiableStatisticalModel`s, which can compute the gradient with respect to their parameters for a given input `Sequence`. The parameters of `DifferentiableStatisticalModel` are learned numerically, typically by gradient-based method like provided in `Optimizer`.
This is especially used in Jstacs for learning the parameters by discriminative learning principles like maximum conditional likelihood or maximum supervised posterior (see `MSPClassifier`) or by a unified learning principle (see `GenDisMixClassifier`).
The sub-package `de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels` contains Bayesian networks and inhomogeneous Markov models.
The sub-package `de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous` provides homogeneous models like homogeneous Markov models.
The sub-package `de.jstacs.sequenceScores.statisticalModels.differentiable.mixture` provides mixture models including an extended ZOOPS model for de-novo motif discovery.
Some of the provided `DifferentiableStatisticalModel`s also implement the interface `SamplingDifferentiableStatisticalModel` and can be used for Metropolis-Hastings parameter sampling in a `SamplingGenDisMixClassifier`.

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