Package de.jstacs.classifier.scoringFunctionBased.logPrior

Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors

See:
Description

Class Summary

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.
SeparateGaussianLogPrior	Class for a LogPrior that defines a Gaussian prior on the parameters of a set of NormalizableScoringFunctions and a set of class parameters.
SeparateLaplaceLogPrior	Class for a LogPrior that defines a Laplace prior on the parameters of a set of NormalizableScoringFunctions and a set of class parameters.
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.

Package de.jstacs.classifier.scoringFunctionBased.logPrior Description

Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors.