Uses of Interface
de.jstacs.sampling.BurnInTest

Packages that use BurnInTest

de.jstacs.classifiers.differentiableSequenceScoreBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingDifferentiableStatisticalModels 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 SamplingDifferentiableStatisticalModels 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.