Package de.jstacs.models.discrete.inhomogeneous

This package contains various inhomogeneous models.

See:
Description

Class Summary

BayesianNetworkModel	The class implements a Bayesian network ( StructureLearner.ModelType.BN ) of fixed order.
CombinationIterator	This class can be used for iterating over all possible combinations (in the sense of combinatorics).
DAGModel	The abstract class for directed acyclic graphical models (DAGModel).
FSDAGModel	This class can be used for any discrete fixed structure directed acyclic graphical model ( FSDAGModel).
FSDAGModelForGibbsSampling	This is the class for a fixed structure directed acyclic graphical model (see FSDAGModel) that can be used in a Gibbs sampling.
InhCondProb	This class handles (conditional) probabilities of sequences for inhomogeneous models.
InhConstraint	This class is the superclass for all inhomogeneous constraints.
InhomogeneousDGM	This class is the main class for all inhomogeneous discrete graphical models (InhomogeneousDGM).
MEMConstraint	This constraint can be used for any maximum entropy model (MEM) application.
SequenceIterator	This class is used to iterate over a discrete sequence.
StructureLearner	This class can be used to learn the structure of any discrete model.
TwoPointEvaluater	This class is for visualizing two point dependency between sequence positions.

Enum Summary

StructureLearner.LearningType	This enum defines the different types of learning that are possible with the StructureLearner.
StructureLearner.ModelType	This enum defines the different types of models that can be learned with the StructureLearner.

Package de.jstacs.models.discrete.inhomogeneous Description

This package contains various inhomogeneous models. The most interesting classes are

1. BayesianNetworkModel, which is either an inhomogeneous Markov model, a permuted Markov model or a Bayesian network
2. FSDAGModel, which is a model on a fixed structure of a directed acyclic graph
3. ...

The parameters for all models is located in the subpackage parameters

See Also:

BayesianNetworkModel, FSDAGModel, de.jstacs.models.discrete.inhomogeneous.parameters