rPosteriorPredictive

A "BayesianBrick" object used to select a method.

further arguments passed to or from other methods.

This is a generic function that will generate random samples from the posterior predictive distribution. i.e. for the model structure: 
 theta|gamma ~ H(gamma) 
 X|theta ~ F(theta) 
generate x_new from the posterior predictive distribution of x|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.

Haotian Chen

rPosteriorPredictive: Generate random samples from the posterior predictive distribution

Description

Usage

Arguments

Value

See Also