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See:
Description
Class Summary | |
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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 | |
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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 . |
This package contains various inhomogeneous models. The most interesting classes are
BayesianNetworkModel
,
FSDAGModel
,
de.jstacs.models.discrete.inhomogeneous.parameters
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