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## Uses of Interface
de.jstacs.sampling.BurnInTest

Packages that use BurnInTest
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.sampling | This package contains many classes that can be used while a sampling.
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 BurnInTest in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Fields in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling declared as BurnInTest
`protected BurnInTest` | `SamplingScoreBasedClassifier.burnInTest` The `BurnInTest`, may be null for no test

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type BurnInTest
`protected double` | `SamplingScoreBasedClassifier.sampleNSteps(DiffSSBasedOptimizableFunction function, SamplingScoreBasedClassifier.DiffSMSamplingComponent component, BurnInTest test, int numSteps, SamplingScoreBasedClassifier.SamplingScheme scheme)` Samples a predefined number of steps appended to the current sampling

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type BurnInTest
`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 BurnInTest in de.jstacs.sampling

Classes in de.jstacs.sampling that implement BurnInTest
`class` | `AbstractBurnInTest` This abstract class implements some of the methods of `BurnInTest` to alleviate the implementation of efficient and new burn-in tests.
`class` | `SimpleBurnInTest` Deprecated. since this burn test ignore the data coming from the sampling, it might be problematic to use this test
`class` | `VarianceRatioBurnInTest` In this class the Variance-Ratio method of Gelman and Rubin is implemented to test the length of the burn-in phase of a multi-chain Gibbs Sampling (number of chains >2).

Methods in de.jstacs.sampling that return BurnInTest
`BurnInTest` | `BurnInTest.clone()` Return a deep copy of this object.

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

Fields in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models declared as BurnInTest
`protected BurnInTest` | `SamplingHigherOrderHMM.burnInTest` This variable holds the BurnInTest used for training the model

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

Fields in de.jstacs.sequenceScores.statisticalModels.trainable.mixture declared as BurnInTest
`protected BurnInTest` | `AbstractMixtureTrainSM.burnInTest` The `BurnInTest` that is used to stop the sampling.

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture with parameters of type BurnInTest
`AbstractMixtureTrainSM(int length, TrainableStatisticalModel[] models, boolean[] optimizeModel, int dimension, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates a new `AbstractMixtureTrainSM`.
`MixtureTrainSM(int length, TrainableStatisticalModel[] models, double[] weights, int starts, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and fixed component probabilities.
`MixtureTrainSM(int length, TrainableStatisticalModel[] models, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates a new `MixtureTrainSM`.
`MixtureTrainSM(int length, TrainableStatisticalModel[] models, int starts, double[] componentHyperParams, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and sampling the component probabilities.
`StrandTrainSM(TrainableStatisticalModel model, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double forwardStrandProb, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates a new `StrandTrainSM`.
`StrandTrainSM(TrainableStatisticalModel model, int starts, double[] componentHyperParams, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and sampling the component probabilities.
`StrandTrainSM(TrainableStatisticalModel model, int starts, double forwardStrandProb, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and fixed component probabilities.

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

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif with parameters of type BurnInTest
`HiddenMotifMixture(TrainableStatisticalModel[] models, boolean[] optimzeArray, int components, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, PositionPrior posPrior, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates a new `HiddenMotifMixture`.
`ZOOPSTrainSM(TrainableStatisticalModel motif, TrainableStatisticalModel bg, boolean trainOnlyMotifModel, int starts, double[] componentHyperParams, double[] weights, PositionPrior posPrior, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates a new `ZOOPSTrainSM`.

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