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

Packages that use BayesianEstimator
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
 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
 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
 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
 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