Uses of Package
gov.sandia.cognition.statistics.bayesian

Packages that use gov.sandia.cognition.statistics.bayesian

Package	Description
gov.sandia.cognition.learning.algorithm.clustering	Provides clustering algorithms.
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.

Classes in gov.sandia.cognition.statistics.bayesian used by gov.sandia.cognition.learning.algorithm.clustering

Class and Description
DirichletProcessMixtureModel An implementation of Dirichlet Process clustering, which estimates the number of clusters and the centroids of the clusters from a set of data.

Classes in gov.sandia.cognition.statistics.bayesian used by gov.sandia.cognition.statistics.bayesian

Class and Description
AbstractKalmanFilter Contains fields useful to both Kalman filters and extended Kalman filters.
AbstractMarkovChainMonteCarlo Partial abstract implementation of MarkovChainMonteCarlo.
AbstractParticleFilter Partial abstract implementation of ParticleFilter.
AdaptiveRejectionSampling Samples form a univariate distribution using the method of adaptive rejection sampling, which is a very efficient method that iteratively improves the rejection and acceptance envelopes in response to additional points.
AdaptiveRejectionSampling.AbstractEnvelope Describes an enveloping function comprised of a sorted sequence of lines
AdaptiveRejectionSampling.LineSegment A line that has a minimum and maximum support (x-axis) value.
AdaptiveRejectionSampling.LogEvaluator Wraps an Evaluator and takes the natural logarithm of the evaluate method
AdaptiveRejectionSampling.Point An InputOutputPair that has a natural ordering according to their input (x-axis) values.
AdaptiveRejectionSampling.UpperEnvelope Constructs the upper envelope for sampling.
BayesianCredibleInterval A Bayesian credible interval defines a bound that a scalar parameter is within the given interval.
BayesianEstimator A type of estimation procedure based on Bayes's rule, which allows us to estimate the uncertainty of parameters given a set of observations that we are given.
BayesianLinearRegression Computes a Bayesian linear estimator for a given feature function and a set of observed data.
BayesianLinearRegression.IncrementalEstimator.SufficientStatistic SufficientStatistic for incremental Bayesian linear regression
BayesianLinearRegression.PredictiveDistribution Creates the predictive distribution for the likelihood of a given point.
BayesianParameter A parameter from a Distribution that has an assumed Distribution of values.
BayesianRegression 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.
BayesianRobustLinearRegression Computes a Bayesian linear estimator for a given feature function given a set of InputOutputPair observed values.
BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic SufficientStatistic for incremental Bayesian linear regression
BayesianRobustLinearRegression.PredictiveDistribution Predictive distribution of future data given the posterior of the weights given the data.
DefaultBayesianParameter Default implementation of BayesianParameter using reflection.
DirichletProcessMixtureModel An implementation of Dirichlet Process clustering, which estimates the number of clusters and the centroids of the clusters from a set of data.
DirichletProcessMixtureModel.DPMMCluster Cluster for a step in the DPMM
DirichletProcessMixtureModel.DPMMLogConditional Container for the log conditional likelihood
DirichletProcessMixtureModel.MultivariateMeanCovarianceUpdater Updater that creates specified clusters with distinct means and covariances
DirichletProcessMixtureModel.MultivariateMeanUpdater Updater that creates specified clusters with identical covariances
DirichletProcessMixtureModel.Sample A sample from the Dirichlet Process Mixture Model.
DirichletProcessMixtureModel.Updater Updater for the DPMM
ExtendedKalmanFilter Implements the Extended Kalman Filter (EKF), which is an extension of the Kalman filter that allows nonlinear motion and observation models.
GaussianProcessRegression 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.
GaussianProcessRegression.PredictiveDistribution Predictive distribution for Gaussian Process Regression.
ImportanceSampling 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.
ImportanceSampling.Updater Updater for ImportanceSampling
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.
MarkovChainMonteCarlo Defines the functionality of a Markov chain Monte Carlo algorithm.
MetropolisHastingsAlgorithm An implementation of the Metropolis-Hastings MCMC algorithm, which is the most general formulation of MCMC but can be slow.
MetropolisHastingsAlgorithm.Updater Creates proposals for the MCMC steps.
ParallelDirichletProcessMixtureModel.ClusterUpdaterTask Tasks that update the values of the clusters for Gibbs sampling
ParallelDirichletProcessMixtureModel.DPMMAssignments Assignments from the DPMM
ParallelDirichletProcessMixtureModel.ObservationAssignmentTask Task that assign observations to cluster indices
ParticleFilter A particle filter aims to estimate a sequence of hidden parameters based on observed data using point-mass estimates of the posterior distribution.
ParticleFilter.Updater Updates the particles.
RecursiveBayesianEstimator 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.
RejectionSampling 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.
RejectionSampling.Updater Updater for ImportanceSampling

Classes in gov.sandia.cognition.statistics.bayesian used by gov.sandia.cognition.statistics.bayesian.conjugate

Class and Description
AbstractBayesianParameter Partial implementation of BayesianParameter
BayesianEstimator A type of estimation procedure based on Bayes's rule, which allows us to estimate the uncertainty of parameters given a set of observations that we are given.
BayesianEstimatorPredictor A BayesianEstimator that can also compute the predictive distribution of new data given the posterior.
BayesianParameter A parameter from a Distribution that has an assumed Distribution of values.
RecursiveBayesianEstimator 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.