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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. |
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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