Skip navigation links

Package de.jstacs.sequenceScores.statisticalModels.trainable

Provides all TrainableStatisticalModels, which can be learned from a single DataSet.

See: Description

Package de.jstacs.sequenceScores.statisticalModels.trainable Description

Provides all TrainableStatisticalModels, 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 TrainableStatisticalModels can be used to build a TrainSMBasedClassifier, which can be used to classify new sequences and which can be evaluated using a ClassifierAssessment.
Skip navigation links