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## Uses of Class
de.jstacs.classifier.scoringFunctionBased.logPrior.LogPrior

Packages that use LogPrior
de.jstacs.classifier.scoringFunctionBased.cll | Provides the implementation of the log conditional likelihood as an `OptimizableFunction` and a classifier that uses log conditional likelihood or supervised posterior to learn the parameters of a set of `ScoringFunctions`
de.jstacs.classifier.scoringFunctionBased.logPrior | Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors

Uses of LogPrior in de.jstacs.classifier.scoringFunctionBased.cll

Fields in de.jstacs.classifier.scoringFunctionBased.cll declared as LogPrior
`protected LogPrior` | `CLLClassifier.prior` The prior that is used in this instance.

Methods in de.jstacs.classifier.scoringFunctionBased.cll with parameters of type LogPrior
`static CLLClassifier[]` | `CLLClassifier.create(CLLClassifierParameterSet params, LogPrior prior, ScoringFunction[]... functions)` This method creates an array of `CLLClassifier`s by using the cross-product of the given `ScoringFunction`s.
`void` | `CLLClassifier.setPrior(LogPrior prior)` This method sets a new prior that should be used for optimization.

Constructors in de.jstacs.classifier.scoringFunctionBased.cll with parameters of type LogPrior
`CLLClassifier(CLLClassifierParameterSet params, LogPrior prior, ScoringFunction... score)` The default constructor that creates a new `CLLClassifier` from a given parameter set, a prior and `ScoringFunction`s for the classes.
`NormConditionalLogLikelihood(ScoringFunction[] score, Sample[] data, double[][] weights, LogPrior prior, boolean norm, boolean freeParams)` The constructor creates an instance of the `NormConditionalLogLikelihood` using the given prior.

Uses of LogPrior in de.jstacs.classifier.scoringFunctionBased.logPrior

Subclasses of LogPrior in de.jstacs.classifier.scoringFunctionBased.logPrior
`class` | `CompositeLogPrior` This class implements a composite prior that can be used for NormalizableScoringFunction.
`class` | `DoesNothingLogPrior` This class defines a `LogPrior` that does not penalize any parameter.
`class` | `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.
`class` | `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.
`class` | `SeparateLogPrior` Abstract class for priors that penalize each parameter value independently and have some variances (and possible means) as hyperparameters.
`class` | `SimpleGaussianSumLogPrior` This class implements a prior that is a product of Gaussian distributions with mean 0 and equal variance for each parameter.

Methods in de.jstacs.classifier.scoringFunctionBased.logPrior that return LogPrior
`abstract LogPrior` | `LogPrior.getNewInstance()` This method returns an empty new instance of the current prior.
`LogPrior` | `DoesNothingLogPrior.getNewInstance()`

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