See: Description
| Interface | Description |
|---|---|
| BayesianEstimator<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>> |
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<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>> |
A BayesianEstimator that can also compute the predictive distribution of
new data given the posterior.
|
| BayesianParameter<ParameterType,ConditionalType extends Distribution<?>,PriorType extends Distribution<ParameterType>> |
A parameter from a Distribution that has an assumed Distribution of
values.
|
| 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.
|
| DirichletProcessMixtureModel.Updater<ObservationType> |
Updater for the DPMM
|
| ImportanceSampling.Updater<ObservationType,ParameterType> |
Updater for ImportanceSampling
|
| MarkovChainMonteCarlo<ObservationType,ParameterType> |
Defines the functionality of a Markov chain Monte Carlo algorithm.
|
| MetropolisHastingsAlgorithm.Updater<ObservationType,ParameterType> |
Creates proposals for the MCMC steps.
|
| 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.
|
| ParticleFilter.Updater<ObservationType,ParameterType> |
Updates the particles.
|
| 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.
|
| RejectionSampling.Updater<ObservationType,ParameterType> |
Updater for ImportanceSampling
|
| Class | Description |
|---|---|
| AbstractBayesianParameter<ParameterType,ConditionalType extends ClosedFormDistribution<?>,PriorType extends Distribution<ParameterType>> |
Partial implementation of BayesianParameter
|
| AbstractKalmanFilter |
Contains fields useful to both Kalman filters and extended Kalman filters.
|
| AbstractMarkovChainMonteCarlo<ObservationType,ParameterType> |
Partial abstract implementation of MarkovChainMonteCarlo.
|
| AbstractParticleFilter<ObservationType,ParameterType> |
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.LineSegment |
A line that has a minimum and maximum support (x-axis) value.
|
| AdaptiveRejectionSampling.LogEvaluator<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>> |
Wraps an Evaluator and takes the natural logarithm of the evaluate method
|
| AdaptiveRejectionSampling.PDFLogEvaluator |
Wraps a PDF so that it returns the logEvaluate method.
|
| AdaptiveRejectionSampling.Point |
An InputOutputPair that has a natural ordering according to their
input (x-axis) values.
|
| BayesianCredibleInterval |
A Bayesian credible interval defines a bound that a scalar parameter is
within the given interval.
|
| BayesianLinearRegression |
Computes a Bayesian linear estimator for a given feature function
and a set of observed data.
|
| BayesianLinearRegression.IncrementalEstimator |
Incremental estimator for BayesianLinearRegression
|
| BayesianRobustLinearRegression |
Computes a Bayesian linear estimator for a given feature function given
a set of InputOutputPair observed values.
|
| BayesianRobustLinearRegression.IncrementalEstimator |
Incremental estimator for BayesianRobustLinearRegression
|
| BayesianUtil |
Contains generally useful utilities for Bayesian statistics.
|
| ConditionalProbability<DataType> |
A class for finding the conditional probability of two elements, or one element and a collection of other elements.
|
| DefaultBayesianParameter<ParameterType,ConditionalType extends ClosedFormDistribution<?>,PriorType extends Distribution<ParameterType>> |
Default implementation of BayesianParameter using reflection.
|
| 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.
|
| DirichletProcessMixtureModel.DPMMCluster<ObservationType> |
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<ObservationType> |
A sample from the Dirichlet Process Mixture Model.
|
| ExtendedKalmanFilter |
Implements the Extended Kalman Filter (EKF), which is an extension of the
Kalman filter that allows nonlinear motion and observation models.
|
| 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.
|
| 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.
|
| ImportanceSampling.DefaultUpdater<ObservationType,ParameterType> |
Default ImportanceSampling Updater that uses a BayesianParameter
to compute the quantities of interest.
|
| 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.
|
| MetropolisHastingsAlgorithm<ObservationType,ParameterType> |
An implementation of the Metropolis-Hastings MCMC algorithm, which is the
most general formulation of MCMC but can be slow.
|
| ParallelDirichletProcessMixtureModel<ObservationType> |
A Parallelized version of vanilla Dirichlet Process Mixture Model learning.
|
| ParallelDirichletProcessMixtureModel.DPMMAssignments |
Assignments from the DPMM
|
| 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.
|
| RejectionSampling.DefaultUpdater<ObservationType,ParameterType> |
Default ImportanceSampling Updater that uses a BayesianParameter
to compute the quantities of interest.
|
| RejectionSampling.ScalarEstimator<ObservationType> |
Routine for estimating the minimum scalar needed to envelop the
conjunctive distribution.
|
| SamplingImportanceResamplingParticleFilter<ObservationType,ParameterType> |
An implementation of the standard Sampling Importance Resampling
particle filter.
|