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## Uses of Package
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Packages that use de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
de.jstacs.sequenceScores.statisticalModels.trainable | Provides all `TrainableStatisticalModel`s, which can be learned from a single `DataSet`.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous | This package contains various inhomogeneous models.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared |

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous used by de.jstacs.sequenceScores.statisticalModels.trainable
BayesianNetworkTrainSM The class implements a Bayesian network ( `StructureLearner.ModelType.BN` ) of fixed order.
FSDAGTrainSM This class can be used for any discrete fixed structure directed acyclic graphical model ( `FSDAGTrainSM`).

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous used by de.jstacs.sequenceScores.statisticalModels.trainable.discrete
InhCondProb This class handles (conditional) probabilities of sequences for inhomogeneous models.
MEMConstraint This constraint can be used for any maximum entropy model (MEM) application.

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous used by de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
BayesianNetworkTrainSM The class implements a Bayesian network ( `StructureLearner.ModelType.BN` ) of fixed order.
DAGTrainSM The abstract class for directed acyclic graphical models (`DAGTrainSM`).
FSDAGModelForGibbsSampling This is the class for a fixed structure directed acyclic graphical model (see `FSDAGTrainSM`) that can be used in a Gibbs sampling.
FSDAGTrainSM This class can be used for any discrete fixed structure directed acyclic graphical model ( `FSDAGTrainSM`).
InhCondProb This class handles (conditional) probabilities of sequences for inhomogeneous models.
InhConstraint This class is the superclass for all inhomogeneous constraints.
InhomogeneousDGTrainSM This class is the main class for all inhomogeneous discrete graphical models (`InhomogeneousDGTrainSM`).
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.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`.

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous used by de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters
FSDAGTrainSM This class can be used for any discrete fixed structure directed acyclic graphical model ( `FSDAGTrainSM`).
InhomogeneousDGTrainSM This class is the main class for all inhomogeneous discrete graphical models (`InhomogeneousDGTrainSM`).
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`.

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous used by de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared
FSDAGTrainSM This class can be used for any discrete fixed structure directed acyclic graphical model ( `FSDAGTrainSM`).
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`.

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