Overview | Package | Class | Use | Tree | Deprecated | Index | Help
PREV NEXT | FRAMES NO FRAMES

## Uses of Class
de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel

Packages that use AbstractTrainableStatisticalModel
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.homogeneous |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous | This package contains various inhomogeneous models.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm | The package provides all interfaces and classes for a hidden Markov model (HMM).
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models | The package provides different implementations of hidden Markov models based on `AbstractHMM`
de.jstacs.sequenceScores.statisticalModels.trainable.mixture | This package is the super package for any mixture model.
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable
`class` | `CompositeTrainSM` This class is for modelling sequences by modelling the different positions of the each sequence by different models.
`class` | `DifferentiableStatisticalModelWrapperTrainSM` This model can be used to use a DifferentiableStatisticalModel as model.
`class` | `UniformTrainSM` This class represents a uniform model.
`class` | `VariableLengthWrapperTrainSM` This class allows to train any `TrainableStatisticalModel` on `DataSet`s of `Sequence`s with variable length if each individual length is at least `SequenceScore.getLength()`.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable that return AbstractTrainableStatisticalModel
`AbstractTrainableStatisticalModel` | `AbstractTrainableStatisticalModel.clone()` Follows the conventions of `Object`'s `clone()`-method.

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete
`class` | `DiscreteGraphicalTrainSM` This is the main class for all discrete graphical models (DGM).

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous
`class` | `HomogeneousMM` This class implements homogeneous Markov models (hMM) of arbitrary order.
`class` | `HomogeneousTrainSM` This class implements homogeneous models of arbitrary order.

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
`class` | `BayesianNetworkTrainSM` The class implements a Bayesian network ( `StructureLearner.ModelType.BN` ) of fixed order.
`class` | `DAGTrainSM` The abstract class for directed acyclic graphical models (`DAGTrainSM`).
`class` | `FSDAGModelForGibbsSampling` This is the class for a fixed structure directed acyclic graphical model (see `FSDAGTrainSM`) that can be used in a Gibbs sampling.
`class` | `FSDAGTrainSM` This class can be used for any discrete fixed structure directed acyclic graphical model ( `FSDAGTrainSM`).
`class` | `InhomogeneousDGTrainSM` This class is the main class for all inhomogeneous discrete graphical models (`InhomogeneousDGTrainSM`).

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared
`class` | `SharedStructureMixture` This class handles a mixture of models with the same structure that is learned via EM.

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.hmm

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.hmm
`class` | `AbstractHMM` This class is the super class of all implementations hidden Markov models (HMMs) in Jstacs.

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models
`class` | `DifferentiableHigherOrderHMM` This class combines an `HigherOrderHMM` and a `DifferentiableStatisticalModel` by implementing some of the declared methods.
`class` | `HigherOrderHMM` This class implements a higher order hidden Markov model.
`class` | `SamplingHigherOrderHMM`
`class` | `SamplingPhyloHMM` This class implements an (higher order) HMM that contains multi-dimensional emissions described by a phylogenetic tree.

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.mixture

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.mixture
`class` | `AbstractMixtureTrainSM` This is the abstract class for all kinds of mixture models.
`class` | `MixtureTrainSM` The class for a mixture model of any `TrainableStatisticalModel`s.
`class` | `StrandTrainSM` This model handles sequences that can either lie on the forward strand or on the reverse complementary strand.

Uses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif

Subclasses of AbstractTrainableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif
`class` | `HiddenMotifMixture` This is the main class that every generative motif discoverer should implement.
`class` | `ZOOPSTrainSM` This class enables the user to search for a single motif in a sequence.

Overview | Package | Class | Use | Tree | Deprecated | Index | Help
PREV NEXT | FRAMES NO FRAMES