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## Uses of Interface
de.jstacs.scoringFunctions.SamplingScoringFunction

Packages that use SamplingScoringFunction
de.jstacs.classifier.scoringFunctionBased.sampling | Provides the classes for `AbstractScoreBasedClassifier`s that are based on `SamplingScoringFunction`s 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 `ScoringFunction`s 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` `SamplingScoringFunction`s

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 `SamplingScoringFunction`s 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.

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