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## 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`).
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`

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