Uses of Class
gov.sandia.cognition.statistics.distribution.UnivariateGaussian

Packages that use UnivariateGaussian

Package	Description
gov.sandia.cognition.learning.data.feature	Provides data feature extractors.
gov.sandia.cognition.learning.function.categorization	Provides functions that output a discrete set of categories.
gov.sandia.cognition.statistics.bayesian	Provides algorithms for computing Bayesian estimates of parameters.
gov.sandia.cognition.statistics.bayesian.conjugate	Provides Bayesian estimation routines based on conjugate prior distribution of parameters of specific conditional distributions.
gov.sandia.cognition.statistics.distribution	Provides statistical distributions.
gov.sandia.cognition.statistics.montecarlo	Provides Monte Carlo procedures for numerical integration and sampling.

Uses of UnivariateGaussian in gov.sandia.cognition.learning.data.feature

Constructors in gov.sandia.cognition.learning.data.feature with parameters of type UnivariateGaussian

Constructor and Description
StandardDistributionNormalizer(UnivariateGaussian gaussian) Creates a new instance of StandardDistributionNormalizer from the given Gaussian.

Uses of UnivariateGaussian in gov.sandia.cognition.learning.function.categorization

Methods in gov.sandia.cognition.learning.function.categorization that return UnivariateGaussian

Modifier and Type	Method and Description
UnivariateGaussian	ConfidenceWeightedBinaryCategorizer.evaluateAsGaussian(Vectorizable input) Returns the univariate Gaussian distribution over the output of the distribution of weight vectors times the input, with the confidence that the categorizer was trained using.
UnivariateGaussian	DefaultConfidenceWeightedBinaryCategorizer.evaluateAsGaussian(Vectorizable input)
UnivariateGaussian	DiagonalConfidenceWeightedBinaryCategorizer.evaluateAsGaussian(Vectorizable input)

Uses of UnivariateGaussian in gov.sandia.cognition.statistics.bayesian

Methods in gov.sandia.cognition.statistics.bayesian that return UnivariateGaussian

Modifier and Type	Method and Description
UnivariateGaussian	BayesianLinearRegression.createConditionalDistribution(Vectorizable input, Vector weights) Creates the distribution from which the outputs are generated, given the weights and the input to consider.
UnivariateGaussian	BayesianRobustLinearRegression.createConditionalDistribution(Vectorizable input, Vector weights) Creates the distribution from which the outputs are generated, given the weights and the input to consider.
UnivariateGaussian	GaussianProcessRegression.PredictiveDistribution.evaluate(InputType input)
static <ObservationType,ParameterType> UnivariateGaussian	BayesianUtil.expectedDeviance(BayesianParameter<ParameterType,? extends ComputableDistribution<ObservationType>,?> predictiveDistribution, java.lang.Iterable<? extends ObservationType> observations, java.util.Random random, int numSamples) Computes the expected deviance of the model by sampling parameters from the posterior and then computing the deviance using the conditional distribution.
static UnivariateGaussian	BayesianUtil.getMean(java.util.Collection<? extends java.lang.Double> samples) Computes the Monte Carlo distribution of the given samples.

Uses of UnivariateGaussian in gov.sandia.cognition.statistics.bayesian.conjugate

Methods in gov.sandia.cognition.statistics.bayesian.conjugate that return UnivariateGaussian

Modifier and Type	Method and Description
UnivariateGaussian	UnivariateGaussianMeanBayesianEstimator.createPredictiveDistribution(UnivariateGaussian posterior) Creates the predictive distribution from the given posterior.

Methods in gov.sandia.cognition.statistics.bayesian.conjugate with parameters of type UnivariateGaussian

Modifier and Type	Method and Description
double	UnivariateGaussianMeanBayesianEstimator.computeEquivalentSampleSize(UnivariateGaussian belief)
UnivariateGaussianMeanVarianceBayesianEstimator.Parameter	UnivariateGaussianMeanVarianceBayesianEstimator.createParameter(UnivariateGaussian conditional, NormalInverseGammaDistribution prior)
UnivariateGaussianMeanBayesianEstimator.Parameter	UnivariateGaussianMeanBayesianEstimator.createParameter(UnivariateGaussian conditional, UnivariateGaussian prior)
UnivariateGaussian	UnivariateGaussianMeanBayesianEstimator.createPredictiveDistribution(UnivariateGaussian posterior) Creates the predictive distribution from the given posterior.
void	UnivariateGaussianMeanBayesianEstimator.update(UnivariateGaussian updater, java.lang.Double data)

Constructors in gov.sandia.cognition.statistics.bayesian.conjugate with parameters of type UnivariateGaussian

Constructor and Description
Parameter(UnivariateGaussian conditional, NormalInverseGammaDistribution prior) Creates a new instance
Parameter(UnivariateGaussian conditional, UnivariateGaussian prior) Creates a new instance
UnivariateGaussianMeanBayesianEstimator(double knownVariance, UnivariateGaussian belief) Creates a new instance of UnivariateGaussianMeanBayesianEstimator
UnivariateGaussianMeanBayesianEstimator(UnivariateGaussian conditional, UnivariateGaussian prior) Creates a new instance
UnivariateGaussianMeanVarianceBayesianEstimator(UnivariateGaussian conditional, NormalInverseGammaDistribution prior) Creates a new instance of UnivariateGaussianMeanVarianceBayesianEstimator

Constructor parameters in gov.sandia.cognition.statistics.bayesian.conjugate with type arguments of type UnivariateGaussian

Constructor and Description
UnivariateGaussianMeanBayesianEstimator(BayesianParameter<java.lang.Double,UnivariateGaussian,UnivariateGaussian> parameter) Creates a new instance
UnivariateGaussianMeanBayesianEstimator(BayesianParameter<java.lang.Double,UnivariateGaussian,UnivariateGaussian> parameter) Creates a new instance
UnivariateGaussianMeanVarianceBayesianEstimator(BayesianParameter<Vector,UnivariateGaussian,NormalInverseGammaDistribution> parameter) Creates a new instance of UnivariateGaussianMeanVarianceBayesianEstimator

Uses of UnivariateGaussian in gov.sandia.cognition.statistics.distribution

Subclasses of UnivariateGaussian in gov.sandia.cognition.statistics.distribution

Modifier and Type	Class and Description
static class	UnivariateGaussian.CDF CDF of the underlying Gaussian.
static class	UnivariateGaussian.CDF.Inverse Inverts the CumulativeDistribution function.
static class	UnivariateGaussian.PDF PDF of the underlying Gaussian.

Methods in gov.sandia.cognition.statistics.distribution that return UnivariateGaussian

Modifier and Type	Method and Description
UnivariateGaussian	UnivariateGaussian.clone()
UnivariateGaussian	UnivariateGaussian.convolve(UnivariateGaussian other) Convolves this Gaussian with the other Gaussian.
UnivariateGaussian	UnivariateGaussian.times(UnivariateGaussian other) Multiplies this Gaussian with the other Gaussian.

Methods in gov.sandia.cognition.statistics.distribution with parameters of type UnivariateGaussian

Modifier and Type	Method and Description
UnivariateGaussian	UnivariateGaussian.convolve(UnivariateGaussian other) Convolves this Gaussian with the other Gaussian.
void	UnivariateGaussian.SufficientStatistic.create(UnivariateGaussian distribution)
UnivariateGaussian	UnivariateGaussian.times(UnivariateGaussian other) Multiplies this Gaussian with the other Gaussian.

Constructors in gov.sandia.cognition.statistics.distribution with parameters of type UnivariateGaussian

Constructor and Description
CDF(UnivariateGaussian other) Copy constructor
Inverse(UnivariateGaussian other) Copy constructor
PDF(UnivariateGaussian other) Copy constructor
UnivariateGaussian(UnivariateGaussian other) Copy constructor

Uses of UnivariateGaussian in gov.sandia.cognition.statistics.montecarlo

Methods in gov.sandia.cognition.statistics.montecarlo that return UnivariateGaussian

Modifier and Type	Method and Description
static UnivariateGaussian	MultivariateCumulativeDistributionFunction.compute(Vector input, Distribution<Vector> distribution, java.util.Random random, double probabilityTolerance) Computes a multi-variate cumulative distribution for a given input according to the given distribution.