Package de.jstacs.models.discrete.inhomogeneous

This package contains various inhomogeneous models.

See:
          Description

Class Summary
BayesianNetworkModel The class implements a Bayesian network of fixed order.
CombinationIterator This class can be used for iterating of all possible combinations
DAGModel The abstract class for directed acyclic graphical models.
FSDAGModel This class can be used for any discrete fixed structure DAG model (FSDAGModel).
InhCondProb This class handles the (conditional) probabilities.
InhConstraint This class is the super class for all inhomogeneous constraints.
InhomogeneousDGM This class is the main class for all inhomgeneous discrete graphical models (IDGM).
MEMConstraint The constraint can be used for any 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