Uses of Package
de.jstacs.classifier.scoringFunctionBased.logPrior

Packages that use de.jstacs.classifier.scoringFunctionBased.logPrior

de.jstacs.classifier.scoringFunctionBased	Provides the classes for Classifiers that are based on ScoringFunctions.
de.jstacs.classifier.scoringFunctionBased.gendismix	Provides an implementation of a classifier that allows to train the parameters of a set of NormalizableScoringFunctions by a unified generative-discriminative learning principle
de.jstacs.classifier.scoringFunctionBased.logPrior	Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors
de.jstacs.classifier.scoringFunctionBased.msp	Provides an implementation of a classifier that allows to train the parameters of a set of ScoringFunctions either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL)
de.jstacs.classifier.scoringFunctionBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingScoringFunctions and that sample parameters using the Metropolis-Hastings algorithm.

Classes in de.jstacs.classifier.scoringFunctionBased.logPrior used by de.jstacs.classifier.scoringFunctionBased

LogPrior
The abstract class for any log-prior used e.g. for maximum supervised posterior optimization.

Classes in de.jstacs.classifier.scoringFunctionBased.logPrior used by de.jstacs.classifier.scoringFunctionBased.gendismix

LogPrior
The abstract class for any log-prior used e.g. for maximum supervised posterior optimization.

Classes in de.jstacs.classifier.scoringFunctionBased.logPrior used by de.jstacs.classifier.scoringFunctionBased.logPrior

CompositeLogPrior
This class implements a composite prior that can be used for NormalizableScoringFunction.

DoesNothingLogPrior
This class defines a LogPrior that does not penalize any parameter.

LogPrior
The abstract class for any log-prior used e.g. for maximum supervised posterior optimization.

SeparateLogPrior
Abstract class for priors that penalize each parameter value independently and have some variances (and possible means) as hyperparameters.

SimpleGaussianSumLogPrior
This class implements a prior that is a product of Gaussian distributions with mean 0 and equal variance for each parameter.

Classes in de.jstacs.classifier.scoringFunctionBased.logPrior used by de.jstacs.classifier.scoringFunctionBased.msp

LogPrior
The abstract class for any log-prior used e.g. for maximum supervised posterior optimization.

Classes in de.jstacs.classifier.scoringFunctionBased.logPrior used by de.jstacs.classifier.scoringFunctionBased.sampling

LogPrior
The abstract class for any log-prior used e.g. for maximum supervised posterior optimization.