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## Uses of Interface
de.jstacs.sequenceScores.statisticalModels.StatisticalModel

Packages that use StatisticalModel
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.discrete |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous | This package contains various inhomogeneous models.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm | The package provides all interfaces and classes for a hidden Markov model (HMM).
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models | The package provides different implementations of hidden Markov models based on `AbstractHMM`
de.jstacs.sequenceScores.statisticalModels.trainable.mixture | This package is the super package for any mixture model.
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |
de.jstacs.utils | This package contains a bundle of useful classes and interfaces like ...

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

Subinterfaces of StatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable
`interface` | `DifferentiableStatisticalModel` The interface for normalizable `DifferentiableSequenceScore`s.
`interface` | `SamplingDifferentiableStatisticalModel` Interface for `DifferentiableStatisticalModel`s that can be used for Metropolis-Hastings sampling in a `SamplingScoreBasedClassifier`.
`interface` | `VariableLengthDiffSM` This is an interface for all `DifferentiableStatisticalModel`s that allow to score subsequences of arbitrary length.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable that implement StatisticalModel
`class` | `AbstractDifferentiableStatisticalModel` This class is the main part of any `ScoreClassifier`.
`class` | `AbstractVariableLengthDiffSM` This abstract class implements some methods declared in `DifferentiableStatisticalModel` based on the declaration of methods in `VariableLengthDiffSM`.
`class` | `CyclicMarkovModelDiffSM` This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
`class` | `IndependentProductDiffSM` This class enables the user to model parts of a sequence independent of each other.
`class` | `MappingDiffSM` This class implements a `DifferentiableStatisticalModel` that works on mapped `Sequence`s.
`class` | `MarkovRandomFieldDiffSM` This class implements the scoring function for any MRF (Markov Random Field).
`class` | `NormalizedDiffSM` This class makes an unnormalized `DifferentiableStatisticalModel` to a normalized `DifferentiableStatisticalModel`.
`class` | `UniformDiffSM` This `DifferentiableStatisticalModel` does nothing.

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

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that implement StatisticalModel
`class` | `BayesianNetworkDiffSM` This class implements a scoring function that is a moral directed graphical model, i.e. a moral Bayesian network.
`class` | `MarkovModelDiffSM` This class implements a `AbstractDifferentiableStatisticalModel` for an inhomogeneous Markov model.

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

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous that implement StatisticalModel
`class` | `HomogeneousDiffSM` This is the main class for all homogeneous `DifferentiableSequenceScore`s.
`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 StatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

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

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif that implement StatisticalModel
`class` | `DurationDiffSM` This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models.
`class` | `ExtendedZOOPSDiffSM` This class handles mixtures with at least one hidden motif.
`class` | `MixtureDurationDiffSM` This class implements a mixture of `DurationDiffSM`s.
`class` | `PositionDiffSM` This class implements a position scoring function that enables the user to get a score without using a Sequence object.
`class` | `SkewNormalLikeDurationDiffSM` This class implements a skew normal like discrete truncated distribution.
`class` | `UniformDurationDiffSM` This scoring function implements a uniform distribution for positions.

Uses of StatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable

Subinterfaces of StatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable
`interface` | `TrainableStatisticalModel` This interface defines all methods for a probabilistic model.

Classes in de.jstacs.sequenceScores.statisticalModels.trainable that implement StatisticalModel
`class` | `AbstractTrainableStatisticalModel` Abstract class for a model for pattern recognition.
`class` | `CompositeTrainSM` This class is for modelling sequences by modelling the different positions of the each sequence by different models.
`class` | `DifferentiableStatisticalModelWrapperTrainSM` This model can be used to use a DifferentiableStatisticalModel as model.
`class` | `UniformTrainSM` This class represents a uniform model.
`class` | `VariableLengthWrapperTrainSM` This class allows to train any `TrainableStatisticalModel` on `DataSet`s of `Sequence`s with variable length if each individual length is at least `SequenceScore.getLength()`.

Uses of StatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete that implement StatisticalModel
`class` | `DiscreteGraphicalTrainSM` This is the main class for all discrete graphical models (DGM).

Uses of StatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous that implement StatisticalModel
`class` | `HomogeneousMM` This class implements homogeneous Markov models (hMM) of arbitrary order.
`class` | `HomogeneousTrainSM` This class implements homogeneous models of arbitrary order.

Uses of StatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous that implement StatisticalModel
`class` | `BayesianNetworkTrainSM` The class implements a Bayesian network ( `StructureLearner.ModelType.BN` ) of fixed order.
`class` | `DAGTrainSM` The abstract class for directed acyclic graphical models (`DAGTrainSM`).
`class` | `FSDAGModelForGibbsSampling` This is the class for a fixed structure directed acyclic graphical model (see `FSDAGTrainSM`) that can be used in a Gibbs sampling.
`class` | `FSDAGTrainSM` This class can be used for any discrete fixed structure directed acyclic graphical model ( `FSDAGTrainSM`).
`class` | `InhomogeneousDGTrainSM` This class is the main class for all inhomogeneous discrete graphical models (`InhomogeneousDGTrainSM`).

Uses of StatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared that implement StatisticalModel
`class` | `SharedStructureMixture` This class handles a mixture of models with the same structure that is learned via EM.

Uses of StatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.hmm

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm that implement StatisticalModel
`class` | `AbstractHMM` This class is the super class of all implementations hidden Markov models (HMMs) in Jstacs.

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

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models that implement StatisticalModel
`class` | `DifferentiableHigherOrderHMM` This class combines an `HigherOrderHMM` and a `DifferentiableStatisticalModel` by implementing some of the declared methods.
`class` | `HigherOrderHMM` This class implements a higher order hidden Markov model.
`class` | `SamplingHigherOrderHMM`
`class` | `SamplingPhyloHMM` This class implements an (higher order) HMM that contains multi-dimensional emissions described by a phylogenetic tree.

Uses of StatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.mixture

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that implement StatisticalModel
`class` | `AbstractMixtureTrainSM` This is the abstract class for all kinds of mixture models.
`class` | `MixtureTrainSM` The class for a mixture model of any `TrainableStatisticalModel`s.
`class` | `StrandTrainSM` This model handles sequences that can either lie on the forward strand or on the reverse complementary strand.

Uses of StatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif that implement StatisticalModel
`class` | `HiddenMotifMixture` This is the main class that every generative motif discoverer should implement.
`class` | `ZOOPSTrainSM` This class enables the user to search for a single motif in a sequence.

Uses of StatisticalModel in de.jstacs.utils

Methods in de.jstacs.utils with parameters of type StatisticalModel
`static DataSet` | `DiscreteInhomogenousDataSetEmitter.emitDataSet(StatisticalModel m, int n)` This method emits a sample with `n`sequences from the discrete inhomogeneous model `m` .``
`static double` | `StatisticalModelTester.getKLDivergence(StatisticalModel m1, StatisticalModel m2, int length)` Returns the Kullback-Leibler-divergence `D(p_m1||p_m2)`.
`static double` | `StatisticalModelTester.getLogLikelihood(StatisticalModel m, DataSet data)` Returns the log-likelihood of a `DataSet` `data` for a given model `m`.
`static double` | `StatisticalModelTester.getLogLikelihood(StatisticalModel m, DataSet data, double[] weights)` Returns the log-likelihood of a `DataSet` `data` for a given model `m`.
`static double` | `StatisticalModelTester.getMarginalDistribution(StatisticalModel m, int[] constraint)` This method computes the marginal distribution for any discrete model `m` and all sequences that fulfill the `constraint` , if possible.
`static double` | `StatisticalModelTester.getMaxOfDeviation(StatisticalModel m1, StatisticalModel m2, int length)` This method computes the maximum deviation between the probabilities for all sequences of `length` for discrete models `m1` and `m2`.
`static double` | `StatisticalModelTester.getShannonEntropy(StatisticalModel m, int length)` This method computes the Shannon entropy for any discrete model `m` and all sequences of `length`, if possible.
`static double` | `StatisticalModelTester.getShannonEntropyInBits(StatisticalModel m, int length)` This method computes the Shannon entropy in bits for any discrete model `m` and all sequences of `length`, if possible.
`static double` | `StatisticalModelTester.getSumOfDeviation(StatisticalModel m1, StatisticalModel m2, int length)` This method computes the sum of deviations between the probabilities for all sequences of `length` for discrete models `m1` and `m2`.
`static double` | `StatisticalModelTester.getSumOfDistribution(StatisticalModel m, int length)` This method computes the marginal distribution for any discrete model `m` and all sequences of `length`, if possible.
`static double` | `StatisticalModelTester.getSymKLDivergence(StatisticalModel m1, StatisticalModel m2, int length)` Returns the difference of the Kullback-Leibler-divergences, i.e.
`static double` | `StatisticalModelTester.getValueOfAIC(StatisticalModel m, DataSet s, int k)` This method computes the value of Akaikes Information Criterion (AIC).
`static double` | `StatisticalModelTester.getValueOfBIC(StatisticalModel m, DataSet s, int k)` This method computes the value of the Bayesian Information Criterion (BIC).

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