Uses of Interface
gov.sandia.cognition.statistics.bayesian.BayesianEstimator

Packages that use BayesianEstimator

Package	Description
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.

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

Subinterfaces of BayesianEstimator in gov.sandia.cognition.statistics.bayesian

Modifier and Type	Interface and Description
interface	BayesianEstimatorPredictor<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>> A BayesianEstimator that can also compute the predictive distribution of new data given the posterior.
interface	BayesianRegression<OutputType,PosteriorType extends Distribution<? extends Vector>> A type of regression algorithm maps a Vector space, and the weights of this Vector space are represented as a posterior distribution given the observed InputOutputPairs.
interface	MarkovChainMonteCarlo<ObservationType,ParameterType> Defines the functionality of a Markov chain Monte Carlo algorithm.
interface	ParticleFilter<ObservationType,ParameterType> A particle filter aims to estimate a sequence of hidden parameters based on observed data using point-mass estimates of the posterior distribution.
interface	RecursiveBayesianEstimator<ObservationType,ParameterType,BeliefType extends Distribution<ParameterType>> A recursive Bayesian estimator is an estimation method that uses the previous belief of the system parameter and a single observation to refine the estimate of the system parameter.

Classes in gov.sandia.cognition.statistics.bayesian that implement BayesianEstimator

Modifier and Type	Class and Description
class	AbstractKalmanFilter Contains fields useful to both Kalman filters and extended Kalman filters.
class	AbstractMarkovChainMonteCarlo<ObservationType,ParameterType> Partial abstract implementation of MarkovChainMonteCarlo.
class	AbstractParticleFilter<ObservationType,ParameterType> Partial abstract implementation of ParticleFilter.
class	BayesianLinearRegression Computes a Bayesian linear estimator for a given feature function and a set of observed data.
static class	BayesianLinearRegression.IncrementalEstimator Incremental estimator for BayesianLinearRegression
class	BayesianRobustLinearRegression Computes a Bayesian linear estimator for a given feature function given a set of InputOutputPair observed values.
static class	BayesianRobustLinearRegression.IncrementalEstimator Incremental estimator for BayesianRobustLinearRegression
class	DirichletProcessMixtureModel<ObservationType> An implementation of Dirichlet Process clustering, which estimates the number of clusters and the centroids of the clusters from a set of data.
class	ExtendedKalmanFilter Implements the Extended Kalman Filter (EKF), which is an extension of the Kalman filter that allows nonlinear motion and observation models.
class	GaussianProcessRegression<InputType> Gaussian Process Regression, is also known as Kriging, is a nonparametric method to interpolate and extrapolate using Bayesian regression, where the expressiveness of the estimator can grow with the data.
class	ImportanceSampling<ObservationType,ParameterType> Importance sampling is a Monte Carlo inference technique where we sample from an easy distribution over the hidden variables (parameters) and then weight the result by the ratio of the likelihood of the parameters given the evidence and the likelihood of generating the parameters.
class	KalmanFilter A Kalman filter estimates the state of a dynamical system corrupted with white Gaussian noise with observations that are corrupted with white Gaussian noise.
class	MetropolisHastingsAlgorithm<ObservationType,ParameterType> An implementation of the Metropolis-Hastings MCMC algorithm, which is the most general formulation of MCMC but can be slow.
class	ParallelDirichletProcessMixtureModel<ObservationType> A Parallelized version of vanilla Dirichlet Process Mixture Model learning.
class	RejectionSampling<ObservationType,ParameterType> Rejection sampling is a method of inferring hidden parameters by using an easy-to-sample-from distribution (times a scale factor) that envelopes another distribution that is difficult to sample from.
class	SamplingImportanceResamplingParticleFilter<ObservationType,ParameterType> An implementation of the standard Sampling Importance Resampling particle filter.

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

Subinterfaces of BayesianEstimator in gov.sandia.cognition.statistics.bayesian.conjugate

Modifier and Type	Interface and Description
interface	ConjugatePriorBayesianEstimator<ObservationType,ParameterType,ConditionalType extends ClosedFormDistribution<ObservationType>,BeliefType extends ClosedFormDistribution<ParameterType>> A Bayesian Estimator that makes use of conjugate priors, which is a mathematical trick when the conditional and the prior result a posterior that is the same type as the prior.
interface	ConjugatePriorBayesianEstimatorPredictor<ObservationType,ParameterType,ConditionalType extends ClosedFormDistribution<ObservationType>,BeliefType extends ClosedFormDistribution<ParameterType>> A conjugate prior estimator that also has a closed-form predictive posterior.

Classes in gov.sandia.cognition.statistics.bayesian.conjugate that implement BayesianEstimator

Modifier and Type	Class and Description
class	AbstractConjugatePriorBayesianEstimator<ObservationType,ParameterType,ConditionalType extends ClosedFormDistribution<ObservationType>,BeliefType extends ClosedFormDistribution<ParameterType>> Partial implementation of ConjugatePriorBayesianEstimator that contains a initial belief (prior) distribution function.
class	BernoulliBayesianEstimator A Bayesian estimator for the parameter of a BernoulliDistribution using the conjugate prior BetaDistribution.
class	BinomialBayesianEstimator A Bayesian estimator for the parameter of a Bernoulli parameter, p, of a BinomialDistribution using the conjugate prior BetaDistribution.
class	ExponentialBayesianEstimator Conjugate prior Bayesian estimator of the "rate" parameter of an Exponential distribution using the conjugate prior Gamma distribution.
class	GammaInverseScaleBayesianEstimator A Bayesian estimator for the scale parameter of a Gamma distribution using the conjugate prior Gamma distribution for the inverse-scale (rate) of the Gamma.
class	MultinomialBayesianEstimator A Bayesian estimator for the parameters of a MultinomialDistribution using its conjugate prior distribution, the DirichletDistribution.
class	MultivariateGaussianMeanBayesianEstimator Bayesian estimator for the mean of a MultivariateGaussian using its conjugate prior, which is also a MultivariateGaussian.
class	MultivariateGaussianMeanCovarianceBayesianEstimator Performs robust estimation of both the mean and covariance of a MultivariateGaussian conditional distribution using the conjugate prior Normal-Inverse-Wishart distribution.
class	PoissonBayesianEstimator A Bayesian estimator for the parameter of a PoissonDistribution using the conjugate prior GammaDistribution.
class	UniformDistributionBayesianEstimator A Bayesian estimator for a conditional Uniform(0,theta) distribution using its conjugate prior Pareto distribution.
class	UnivariateGaussianMeanBayesianEstimator Bayesian estimator for the mean of a UnivariateGaussian using its conjugate prior, which is also a UnivariateGaussian.
class	UnivariateGaussianMeanVarianceBayesianEstimator Computes the mean and variance of a univariate Gaussian using the conjugate prior NormalInverseGammaDistribution