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sampleDiscreteRandomVariable(double[], Random) - Static method in class de.jstacs.utils.algorithms.BasicSamplingAlgorithms
Samples a random variable according to a given probability distribution.
sampleDiscreteRandomVariableFromLogProbs(double[], Random) - Static method in class de.jstacs.utils.algorithms.BasicSamplingAlgorithms
Samples a random variable according to a given log probability distribution.
sampleFilePath - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.AbstractVariableStructureModel
 
sampleStorageDirectory - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.latentVariables.AbstractLatentVariableModel
 
sampleStructure(DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.ParsimoniousElement
 
sampleStructureAndParameters(DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.ParsimoniousElement
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
 
samplingStopped() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.AbstractParsimoniousModel
 
saveCurrentParametersToFile() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.AbstractVariableStructureModel
 
saveCurrentParametersToFile() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.AbstractParsimoniousModel
 
setElementNumber(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.ParsimoniousElement
 
setModelType(ParsimoniousElement.ModelType) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.AbstractParsimoniousModel
 
setParameterEstimate(ParsimoniousElement.ParameterEstimate) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.AbstractParsimoniousModel
 
setSampleFilePath(String) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.inhomogeneous.InhomogeneousPMM
 
setSampleStorageDirectory(String) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.latentVariables.AbstractLatentVariableModel
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
 
setScore(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.SymbolSet
 
setStructureScore(ParsimoniousElement.StructureScore) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.AbstractParsimoniousModel
 
setTextOutput(boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.latentVariables.ZOOPSModel
 
size() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.SymbolSet
 
SymbolSet - Class in de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures
 
SymbolSet(DiscreteAlphabet, String...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.SymbolSet
Creates arbitrary sets of symbols
SymbolSet(DiscreteAlphabet, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.variableStructure.parsimonious.dataStructures.SymbolSet
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
Computes the Szpankowski approximation of the multinomal NML
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