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## Package 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.

See:
Description

Class Summary
SamplingGenDisMixClassifier | A classifier that samples its parameters from a `LogGenDisMixFunction` using the Metropolis-Hastings algorithm.
SamplingGenDisMixClassifierParameterSet | `ParameterSet` to instantiate a `SamplingGenDisMixClassifier`.
SamplingScoreBasedClassifier | A classifier that samples the parameters of `SamplingDifferentiableStatisticalModel`s by the Metropolis-Hastings algorithm.
SamplingScoreBasedClassifierParameterSet | `ParameterSet` to instantiate a `SamplingScoreBasedClassifier`.

Enum Summary
SamplingScoreBasedClassifier.SamplingScheme | Sampling scheme for sampling the parameters of the scoring functions.

## Package de.jstacs.classifiers.differentiableSequenceScoreBased.sampling Description

Provides the classes for `AbstractScoreBasedClassifier`s that are based on `SamplingDifferentiableStatisticalModel`s and that sample parameters using the Metropolis-Hastings algorithm. The abstract base class of all such classifiers is the `SamplingScoreBasedClassifier`, while the `SamplingGenDisMixClassifier` provides an implementation for the unified generative-discriminative learning principle.

Since:
  Jstacs 1.5

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