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

Packages that use StatisticalModel

de.jstacs.sequenceScores.statisticalModels.differentiable	Provides all DifferentiableStatisticalModels, which can compute the gradient with respect to their parameters for a given input Sequence.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels	Provides DifferentiableStatisticalModels that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous	Provides DifferentiableStatisticalModels that are homogeneous, i.e.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture	Provides DifferentiableSequenceScores that are mixtures of other DifferentiableSequenceScores.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
de.jstacs.sequenceScores.statisticalModels.trainable	Provides all TrainableStatisticalModels, 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 DifferentiableSequenceScores.
interface	SamplingDifferentiableStatisticalModel Interface for DifferentiableStatisticalModels that can be used for Metropolis-Hastings sampling in a SamplingScoreBasedClassifier.
interface	VariableLengthDiffSM This is an interface for all DifferentiableStatisticalModels 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 Sequences.
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.
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 DifferentiableSequenceScores.
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 DurationDiffSMs.
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 DataSets of Sequences 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	FSMEManager This class can be used for any discrete fixed structure maximum entropy model (FSMEM).
class	InhomogeneousDGTrainSM This class is the main class for all inhomogeneous discrete graphical models (InhomogeneousDGTrainSM).
class	MEManager This class is the super class for all maximum entropy models

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 TrainableStatisticalModels.
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 data set 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).