Uses of Class de.jstacs.classifier.scoringFunctionBased.sampling.SamplingGenDisMixClassifierParameterSet

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Uses of Class
de.jstacs.classifier.scoringFunctionBased.sampling.SamplingGenDisMixClassifierParameterSet

Packages that use SamplingGenDisMixClassifierParameterSet
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.

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

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

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

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.