Uses of Interface
de.jstacs.scoringFunctions.SamplingScoringFunction

Packages that use SamplingScoringFunction

de.jstacs.classifier.scoringFunctionBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingScoringFunctions and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.models.hmm.models	The package provides different implementations of hidden Markov models based on AbstractHMM
de.jstacs.scoringFunctions.directedGraphicalModels	Provides ScoringFunctions that are equivalent to directed graphical models.

Uses of SamplingScoringFunction in de.jstacs.classifier.scoringFunctionBased.sampling

Fields in de.jstacs.classifier.scoringFunctionBased.sampling declared as SamplingScoringFunction

protected SamplingScoringFunction[]

SamplingScoreBasedClassifier.scoringFunctions SamplingScoringFunctions

Constructors in de.jstacs.classifier.scoringFunctionBased.sampling with parameters of type SamplingScoringFunction

SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet params, BurnInTest burnInTest, double[] classVariances, LogPrior prior, double[] beta, SamplingScoringFunction... scoringFunctions)
Creates a new SamplingGenDisMixClassifier using the external parameters params, a burn-in test, a set of sampling variances for the different classes, a prior on the parameters, weights beta for the three components of the LogGenDisMixFunction, i.e., likelihood, conditional likelihood, and prior, and scoring functions that model the distribution for each of the classes.

SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet params, BurnInTest burnInTest, double[] classVariances, LogPrior prior, LearningPrinciple principle, SamplingScoringFunction... scoringFunctions)
Creates a new SamplingGenDisMixClassifier using the external parameters params, a burn-in test, a set of sampling variances for the different classes, a prior on the parameters, a learning principle, and scoring functions that model the distribution for each of the classes.

SamplingScoreBasedClassifier(SamplingScoreBasedClassifierParameterSet params, BurnInTest burnInTest, double[] classVariances, SamplingScoringFunction... scoringFunctions)
Creates a new SamplingScoreBasedClassifier using the parameters in params, a specified BurnInTest (or null for no burn-in test), a set of sampling variances, which may be different for each of the classes (in analogy to equivalent sample size for the Dirichlet distribution), and set set of SamplingScoringFunctions for each of the classes.

Uses of SamplingScoringFunction in de.jstacs.models.hmm.models

Classes in de.jstacs.models.hmm.models that implement SamplingScoringFunction

class

DifferentiableHigherOrderHMM This class combines an HigherOrderHMM and a NormalizableScoringFunction by implementing some of the declared methods.

Uses of SamplingScoringFunction in de.jstacs.scoringFunctions.directedGraphicalModels

Classes in de.jstacs.scoringFunctions.directedGraphicalModels that implement SamplingScoringFunction

class

MutableMarkovModelScoringFunction This class implements a AbstractNormalizableScoringFunction for an inhomogeneous Markov model.