DifferentiableStatisticalModel
s, which can compute the gradient with
respect to their parameters for a given input Sequence
.See: Description
Interface  Description 

DifferentiableStatisticalModel 
The interface for normalizable
DifferentiableSequenceScore s. 
SamplingDifferentiableStatisticalModel 
Interface for
DifferentiableStatisticalModel s that can be used for
MetropolisHastings sampling in a SamplingScoreBasedClassifier . 
VariableLengthDiffSM 
This is an interface for all
DifferentiableStatisticalModel s that allow to score
subsequences of arbitrary length. 
Class  Description 

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).

NormalizedDiffSM 
This class makes an unnormalized
DifferentiableStatisticalModel to a normalized DifferentiableStatisticalModel . 
UniformDiffSM 
This
DifferentiableStatisticalModel does nothing. 
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
gradientbased method like provided in Optimizer
.MSPClassifier
) or by a unified learning principle (see
GenDisMixClassifier
).de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels
contains Bayesian networks and inhomogeneous Markov models.de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous
provides homogeneous models like homogeneous Markov models.de.jstacs.sequenceScores.statisticalModels.differentiable.mixture
provides mixture models including an extended ZOOPS model
for denovo motif discovery.DifferentiableStatisticalModel
s also implement the interface
SamplingDifferentiableStatisticalModel
and can be used for
MetropolisHastings parameter sampling in a SamplingGenDisMixClassifier
.