TrainableStatisticalModel
s, which can
be learned from a single DataSet
.See: Description
Interface  Description 

TrainableStatisticalModel 
This interface defines all methods for a probabilistic model.

Class  Description 

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.

PFMWrapperTrainSM 
A wrapper class for representing position weight matrices or position frequency matrices
from databases as
TrainableStatisticalModel s. 
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() . 
TrainableStatisticalModel
s, which can
be learned from a single DataSet
. Often, parameter learning follows a learning principle
like maximum likelihood or maximum aposteriori. Parameter learning typically is performed analytically like for the homogeneous and inhomogeneous
models in the de.jstacs.sequenceScores.statisticalModels.trainable.discrete
subpackage.de.jstacs.sequenceScores.statisticalModels.trainable.hmm
), which are learned by BaumWelch or Viterbi training,
and mixture models (de.jstacs.sequenceScores.statisticalModels.trainable.mixture
), which are learned by expectationmaximization (EM) or
Gibbs sampling.TrainableStatisticalModel
has been trained, it can be used
to compute the likelihood of new sequences.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
.