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## Uses of Class
de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.LogPrior

Packages that use LogPrior
de.jstacs.classifiers.differentiableSequenceScoreBased | Provides the classes for `Classifier`s that are based on `SequenceScore`s.
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix | Provides an implementation of a classifier that allows to train the parameters of a set of `DifferentiableStatisticalModel`s by a unified generative-discriminative learning principle
de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior | Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors
de.jstacs.classifiers.differentiableSequenceScoreBased.msp | Provides an implementation of a classifier that allows to train the parameters of a set of `DifferentiableStatisticalModel`s either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL)
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling | Provides the classes for `AbstractScoreBasedClassifier`s that are based on `SamplingDifferentiableStatisticalModel`s and that sample parameters using the Metropolis-Hastings algorithm.

Uses of LogPrior in de.jstacs.classifiers.differentiableSequenceScoreBased

Fields in de.jstacs.classifiers.differentiableSequenceScoreBased declared as LogPrior
`protected LogPrior` | `DiffSSBasedOptimizableFunction.prior` The prior that is used in this function.

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased with parameters of type LogPrior
`DiffSSBasedOptimizableFunction(int threads, DifferentiableSequenceScore[] score, DataSet[] data, double[][] weights, LogPrior prior, boolean norm, boolean freeParams)` Creates an instance with the underlying infrastructure.

Uses of LogPrior in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix

Fields in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix declared as LogPrior
`protected LogPrior` | `GenDisMixClassifier.prior` The prior that is used in this classifier.

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type LogPrior
`static GenDisMixClassifier[]` | `GenDisMixClassifier.create(GenDisMixClassifierParameterSet params, LogPrior prior, double[] weights, DifferentiableStatisticalModel[]... functions)` This method creates an array of GenDisMixClassifiers by using the cross-product of the given `DifferentiableStatisticalModel`s.
`void` | `GenDisMixClassifier.setPrior(LogPrior prior)` This method set a new prior that should be used for optimization.

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type LogPrior
`GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double[] beta, DifferentiableStatisticalModel... score)` The main constructor.
`GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double lastScore, double[] beta, DifferentiableSequenceScore... score)` This constructor creates an instance and sets the value of the last (external) optimization.
`GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double lastScore, double[] beta, DifferentiableStatisticalModel... score)` This constructor creates an instance and sets the value of the last (external) optimization.
`GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double genBeta, double disBeta, double priorBeta, DifferentiableStatisticalModel... score)` This convenience constructor agglomerates the `genBeta, disBeta,` and `priorBeta` into an array and calls the main constructor.
`GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, LearningPrinciple key, DifferentiableStatisticalModel... score)` This convenience constructor creates an array of weights for an elementary learning principle and calls the main constructor.
`LogGenDisMixFunction(int threads, DifferentiableSequenceScore[] score, DataSet[] data, double[][] weights, LogPrior prior, double[] beta, boolean norm, boolean freeParams)` The constructor for creating an instance that can be used in an `Optimizer`.
`OneDataSetLogGenDisMixFunction(int threads, DifferentiableSequenceScore[] score, DataSet data, double[][] weights, LogPrior prior, double[] beta, boolean norm, boolean freeParams)` The constructor for creating an instance that can be used in an `Optimizer`.

Uses of LogPrior in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior

Subclasses of LogPrior in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior
`class` | `CompositeLogPrior` This class implements a composite prior that can be used for DifferentiableStatisticalModel.
`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 `DifferentiableStatisticalModel`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 `DifferentiableStatisticalModel`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.classifiers.differentiableSequenceScoreBased.logPrior that return LogPrior
`abstract LogPrior` | `LogPrior.getNewInstance()` This method returns an empty new instance of the current prior.
`LogPrior` | `DoesNothingLogPrior.getNewInstance()`

Uses of LogPrior in de.jstacs.classifiers.differentiableSequenceScoreBased.msp

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.msp with parameters of type LogPrior
`MSPClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, DifferentiableSequenceScore... score)` The default constructor that creates a new `MSPClassifier` from a given parameter set, a prior and `DifferentiableSequenceScore`s for the classes.
`MSPClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double lastScore, DifferentiableSequenceScore... score)` This constructor that creates a new `MSPClassifier` from a given parameter set, a prior and `DifferentiableSequenceScore`s for the classes.

Uses of LogPrior in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type LogPrior
`SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet params, BurnInTest burnInTest, double[] classVariances, LogPrior prior, double[] beta, SamplingDifferentiableStatisticalModel... 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, SamplingDifferentiableStatisticalModel... 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.

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