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## 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
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 `NormalizableScoringFunction`s and a set of class-parameters.
SeparateLaplaceLogPrior | Class for a `LogPrior` that defines a Laplace-prior on the parameters of a set of `NormalizableScoringFunction`s and a set of class-parameters.
SeparateLogPrior | Abstract class for priors that penalize each parameter value independently and have some variance (and possible mean) as hyper-parameters.
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.

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