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## Package de.jstacs.sequenceScores.statisticalModels.trainable

Provides all `TrainableStatisticalModel`s, which can be learned from a single `DataSet`.

See:
Description

Interface Summary
TrainableStatisticalModel | This interface defines all methods for a probabilistic model.

Class Summary
AbstractTrainableStatisticalModel | Abstract class for a model for pattern recognition.
CompositeTrainSM | This class is for modelling sequences by modelling the different positions of the each sequence by different models.
DifferentiableStatisticalModelWrapperTrainSM | This model can be used to use a DifferentiableStatisticalModel as model.
TrainableStatisticalModelFactory | This class allows to easily create some frequently used models.
UniformTrainSM | This class represents a uniform model.
VariableLengthWrapperTrainSM | This class allows to train any `TrainableStatisticalModel` on `DataSet`s of `Sequence`s with variable length if each individual length is at least `SequenceScore.getLength()`.

## Package de.jstacs.sequenceScores.statisticalModels.trainable Description

Provides all `TrainableStatisticalModel`s, which can be learned from a single `DataSet`. Often, parameter learning follows a learning principle like maximum likelihood or maximum a-posteriori. Parameter learning typically is performed analytically like for the homogeneous and inhomogeneous models in the `de.jstacs.sequenceScores.statisticalModels.trainable.discrete` sub-package.
Notable exceptions are hidden Markov models (`de.jstacs.sequenceScores.statisticalModels.trainable.hmm`), which are learned by Baum-Welch or Viterbi training, and mixture models (`de.jstacs.sequenceScores.statisticalModels.trainable.mixture`), which are learned by expectation-maximization (EM) or Gibbs sampling.
After a `TrainableStatisticalModel` has been trained, it can be used to compute the likelihood of new sequences.
Any combination of `TrainableStatisticalModel`s can be used to build a `TrainSMBasedClassifier`, which can be used to classify new sequences and which can be evaluated using a `ClassifierAssessment`.

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