Package | Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
VectorNaiveBayesCategorizer<CategoryType,UnivariateGaussian.PDF> |
VectorNaiveBayesCategorizer.BatchGaussianLearner.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,CategoryType>> data) |
Modifier and Type | Method and Description |
---|---|
UnivariateGaussian.PDF |
RejectionSampling.DefaultUpdater.computeGaussianSampler(java.lang.Iterable<? extends ObservationType> data,
java.util.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) |
Modifier and Type | Method and Description |
---|---|
UnivariateGaussian.PDF |
UnivariateGaussian.SufficientStatistic.create() |
UnivariateGaussian.PDF |
UnivariateGaussian.CDF.getDerivative() |
UnivariateGaussian.PDF |
UnivariateGaussian.getProbabilityFunction() |
UnivariateGaussian.PDF |
UnivariateGaussian.PDF.getProbabilityFunction() |
UnivariateGaussian.PDF |
UnivariateGaussian.MaximumLikelihoodEstimator.learn(java.util.Collection<? extends java.lang.Double> data)
Creates a new instance of UnivariateGaussian from the given data
|
static UnivariateGaussian.PDF |
UnivariateGaussian.MaximumLikelihoodEstimator.learn(java.util.Collection<? extends java.lang.Number> data,
double defaultVariance)
Creates a new instance of UnivariateGaussian from the given data
|
UnivariateGaussian.PDF |
UnivariateGaussian.WeightedMaximumLikelihoodEstimator.learn(java.util.Collection<? extends WeightedValue<? extends java.lang.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(java.util.Collection<? extends WeightedValue<? extends java.lang.Number>> data,
double defaultVariance)
Creates a new instance of UnivariateGaussian using a weighted
Maximum Likelihood estimate based on the given data
|
Modifier and Type | Method and Description |
---|---|
UnivariateGaussian.PDF |
UnivariateMonteCarloIntegrator.getMean(java.util.Collection<? extends java.lang.Double> samples) |
UnivariateGaussian.PDF |
UnivariateMonteCarloIntegrator.getMean(java.util.List<? extends WeightedValue<? extends java.lang.Double>> samples) |
<SampleType> |
UnivariateMonteCarloIntegrator.integrate(java.util.Collection<? extends SampleType> samples,
Evaluator<? super SampleType,? extends java.lang.Double> expectationFunction) |
<SampleType> |
UnivariateMonteCarloIntegrator.integrate(java.util.List<? extends WeightedValue<? extends SampleType>> samples,
Evaluator<? super SampleType,? extends java.lang.Double> expectationFunction) |