- sampleDiscreteRandomVariable(double[], Random) - Static method in class de.jstacs.utils.algorithms.BasicSamplingAlgorithms
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Samples a random variable according to a given probability distribution.
- sampleDiscreteRandomVariableFromLogProbs(double[], Random) - Static method in class de.jstacs.utils.algorithms.BasicSamplingAlgorithms
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Samples a random variable according to a given log probability distribution.
- sampleFilePath - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.AbstractVariableStructureModel
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- sampleStorageDirectory - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.latentVariables.AbstractLatentVariableModel
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- sampleStructure(DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.ParsimoniousElement
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- sampleStructureAndParameters(DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.ParsimoniousElement
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Samples a new context tree structure and corresponding probability parameters given (weighted) input data
- samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.AbstractVariableStructureModel
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- samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.AbstractParsimoniousModel
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- saveCurrentParametersToFile() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.AbstractVariableStructureModel
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- saveCurrentParametersToFile() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.AbstractParsimoniousModel
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- setElementNumber(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.ParsimoniousElement
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- setModelType(ParsimoniousElement.ModelType) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.AbstractParsimoniousModel
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- setParameterEstimate(ParsimoniousElement.ParameterEstimate) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.AbstractParsimoniousModel
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- setSampleFilePath(String) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.inhomogeneous.InhomogeneousPMM
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- setSampleStorageDirectory(String) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.latentVariables.AbstractLatentVariableModel
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Method for setting a directiory of samples parameter sets, i.e., for writing Gibbs sampling output
- setScore(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.Partition
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- setScore(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.SymbolSet
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- setStructureScore(ParsimoniousElement.StructureScore) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.AbstractParsimoniousModel
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- setTextOutput(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.latentVariables.ZOOPSModel
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- size() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.SymbolSet
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- SymbolSet - Class in de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures
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- SymbolSet(DiscreteAlphabet, String...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.SymbolSet
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Creates arbitrary sets of symbols
- SymbolSet(DiscreteAlphabet, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.SymbolSet
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Creates an empty symbol set or one that contains all elements of the alphabet
- SzpankowskiApproximation(int, int) - Static method in class de.jstacs.utils.functions.NML
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Computes the Szpankowski approximation of the multinomal NML