This interface defines all methods for a probabilistic model.
Abstract class for a model for pattern recognition.
This class is for modelling sequences by modelling the different positions of the each sequence by different models.
This model can be used to use a DifferentiableStatisticalModel as model.
A wrapper class for representing position weight matrices or position frequency matrices from databases as
This class allows to easily create some frequently used models.
This class represents a uniform model.
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.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.
TrainableStatisticalModelhas been trained, it can be used to compute the likelihood of new sequences.
TrainableStatisticalModels can be used to build a
TrainSMBasedClassifier, which can be used to classify new sequences and which can be evaluated using a