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

Packages that use UnivariateGaussian.PDF
gov.sandia.cognition.learning.algorithm.bayes Provides algorithms for computing Bayesian categorizers. 
gov.sandia.cognition.statistics.bayesian Provides algorithms for computing Bayesian estimates of parameters. 
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.PDF in gov.sandia.cognition.learning.algorithm.bayes
 

Methods in gov.sandia.cognition.learning.algorithm.bayes that return types with arguments of type UnivariateGaussian.PDF
 VectorNaiveBayesCategorizer<CategoryType,UnivariateGaussian.PDF> VectorNaiveBayesCategorizer.BatchGaussianLearner.learn(Collection<? extends InputOutputPair<? extends Vectorizable,CategoryType>> data)
           
 

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

Methods in gov.sandia.cognition.statistics.bayesian that return UnivariateGaussian.PDF
 UnivariateGaussian.PDF RejectionSampling.DefaultUpdater.computeGaussianSampler(Iterable<? extends ObservationType> data, Random random, int numSamples)
          Computes a Gaussian sample for the parameter, assuming it has is a Double, using importance sampling.
 UnivariateGaussian.PDF BayesianLinearRegression.PredictiveDistribution.evaluate(Vectorizable input)
           
 

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

Methods in gov.sandia.cognition.statistics.distribution that return UnivariateGaussian.PDF
 UnivariateGaussian.PDF UnivariateGaussian.SufficientStatistic.create()
           
 UnivariateGaussian.PDF UnivariateGaussian.CDF.getDerivative()
           
 UnivariateGaussian.PDF UnivariateGaussian.getProbabilityFunction()
           
 UnivariateGaussian.PDF UnivariateGaussian.PDF.getProbabilityFunction()
           
 UnivariateGaussian.PDF UnivariateGaussian.MaximumLikelihoodEstimator.learn(Collection<? extends Double> data)
          Creates a new instance of UnivariateGaussian from the given data
static UnivariateGaussian.PDF UnivariateGaussian.MaximumLikelihoodEstimator.learn(Collection<? extends Number> data, double defaultVariance)
          Creates a new instance of UnivariateGaussian from the given data
 UnivariateGaussian.PDF UnivariateGaussian.WeightedMaximumLikelihoodEstimator.learn(Collection<? extends WeightedValue<? extends Double>> data)
          Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data
static UnivariateGaussian.PDF UnivariateGaussian.WeightedMaximumLikelihoodEstimator.learn(Collection<? extends WeightedValue<? extends Number>> data, double defaultVariance)
          Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data
 

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

Methods in gov.sandia.cognition.statistics.montecarlo that return UnivariateGaussian.PDF
 UnivariateGaussian.PDF UnivariateMonteCarloIntegrator.getMean(Collection<? extends Double> samples)
           
 UnivariateGaussian.PDF UnivariateMonteCarloIntegrator.getMean(List<? extends WeightedValue<? extends Double>> samples)
           
<SampleType>
UnivariateGaussian.PDF
UnivariateMonteCarloIntegrator.integrate(Collection<? extends SampleType> samples, Evaluator<? super SampleType,? extends Double> expectationFunction)
           
<SampleType>
UnivariateGaussian.PDF
UnivariateMonteCarloIntegrator.integrate(List<? extends WeightedValue<? extends SampleType>> samples, Evaluator<? super SampleType,? extends Double> expectationFunction)