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

## Uses of Interface
de.jstacs.sequenceScores.statisticalModels.differentiable.SamplingDifferentiableStatisticalModel

Packages that use SamplingDifferentiableStatisticalModel
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling | Provides the classes for `AbstractScoreBasedClassifier`s that are based on `SamplingDifferentiableStatisticalModel`s and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.sequenceScores.statisticalModels.differentiable | Provides all `DifferentiableStatisticalModel`s, which can compute the gradient with respect to their parameters for a given input `Sequence`.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels | Provides `DifferentiableStatisticalModel`s that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous | Provides `DifferentiableStatisticalModel`s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture | Provides `DifferentiableSequenceScore`s that are mixtures of other `DifferentiableSequenceScore`s.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models | The package provides different implementations of hidden Markov models based on `AbstractHMM`

Uses of SamplingDifferentiableStatisticalModel in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Fields in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling declared as SamplingDifferentiableStatisticalModel
`protected SamplingDifferentiableStatisticalModel[]` | `SamplingScoreBasedClassifier.scoringFunctions` `SamplingDifferentiableStatisticalModel`s

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type SamplingDifferentiableStatisticalModel
`SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet params, BurnInTest burnInTest, double[] classVariances, LogPrior prior, double[] beta, SamplingDifferentiableStatisticalModel... scoringFunctions)` Creates a new `SamplingGenDisMixClassifier` using the external parameters `params`, a burn-in test, a set of sampling variances for the different classes, a prior on the parameters, weights `beta` for the three components of the `LogGenDisMixFunction`, i.e., likelihood, conditional likelihood, and prior, and scoring functions that model the distribution for each of the classes.
`SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet params, BurnInTest burnInTest, double[] classVariances, LogPrior prior, LearningPrinciple principle, SamplingDifferentiableStatisticalModel... scoringFunctions)` Creates a new `SamplingGenDisMixClassifier` using the external parameters `params`, a burn-in test, a set of sampling variances for the different classes, a prior on the parameters, a learning principle, and scoring functions that model the distribution for each of the classes.
`SamplingScoreBasedClassifier(SamplingScoreBasedClassifierParameterSet params, BurnInTest burnInTest, double[] classVariances, SamplingDifferentiableStatisticalModel... scoringFunctions)` Creates a new `SamplingScoreBasedClassifier` using the parameters in `params`, a specified `BurnInTest` (or `null` for no burn-in test), a set of sampling variances, which may be different for each of the classes (in analogy to equivalent sample size for the Dirichlet distribution), and set set of `SamplingDifferentiableStatisticalModel`s for each of the classes.

Uses of SamplingDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable that implement SamplingDifferentiableStatisticalModel
`class` | `CyclicMarkovModelDiffSM` This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
`class` | `UniformDiffSM` This `DifferentiableStatisticalModel` does nothing.

Uses of SamplingDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that implement SamplingDifferentiableStatisticalModel
`class` | `MarkovModelDiffSM` This class implements a `AbstractDifferentiableStatisticalModel` for an inhomogeneous Markov model.

Uses of SamplingDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous that implement SamplingDifferentiableStatisticalModel
`class` | `HomogeneousDiffSM` This is the main class for all homogeneous `DifferentiableSequenceScore`s.
`class` | `HomogeneousMM0DiffSM` This scoring function implements a homogeneous Markov model of order zero (hMM(0)) for a fixed sequence length.
`class` | `HomogeneousMMDiffSM` This scoring function implements a homogeneous Markov model of arbitrary order for any sequence length.
`class` | `UniformHomogeneousDiffSM` This scoring function does nothing.

Uses of SamplingDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture that implement SamplingDifferentiableStatisticalModel
`class` | `AbstractMixtureDiffSM` This main abstract class for any mixture scoring function (e.g.
`class` | `MixtureDiffSM` This class implements a real mixture model.
`class` | `StrandDiffSM` This class enables the user to search on both strand.
`class` | `VariableLengthMixtureDiffSM` This class implements a mixture of `VariableLengthDiffSM` by extending `MixtureDiffSM` and implementing the methods of `VariableLengthDiffSM`.

Uses of SamplingDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif that implement SamplingDifferentiableStatisticalModel
`class` | `ExtendedZOOPSDiffSM` This class handles mixtures with at least one hidden motif.

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

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models that implement SamplingDifferentiableStatisticalModel
`class` | `DifferentiableHigherOrderHMM` This class combines an `HigherOrderHMM` and a `DifferentiableStatisticalModel` by implementing some of the declared methods.

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