posterior

A "BayesianBrick" object used to select a method.

further arguments passed to or from other methods.

This is a generic function that will update the prior distribution of a "BayesianBrick" object by adding information of the observation's sufficient statistics. i.e. for the model structure: 
 theta|gamma ~ H(gamma) 
 X|theta ~ F(theta) 
update gamma to gamma_posterior by adding the information of X to gamma

A class of frequently used Bayesian parametric and nonparametric model structures, as well as a set of tools for common analytical tasks. Structures include Gaussian and Normal-Inverse-Wishart conjugate structure, Gaussian and Normal-Inverse-Gamma conjugate structure, Categorical and Dirichlet conjugate structure, Dirichlet Process on positive integers, Dirichlet Process in general, Hierarchical Dirichlet Process ... Tasks include updating posteriors, calculating marginal likelihood, calculating posterior predictive densities, sampling from posterior predictive distributions, calculating "Maximum A Posteriori" (MAP) estimates ... See Murphy (2012, <doi:10.1080/09332480.2014.914768>), Koller and Friedman (2009, <doi:10.1017/s0269888910000275>) and Andrieu, de Freitas, Doucet and Jordan (2003, <doi:10.1023/A:1020281327116>) for more information. See <https://chenhaotian.github.io/Bayesian-Bricks/> to get started.

posterior: update the prior distribution with sufficient statistics

Description

Usage

Arguments

Value

See Also