rPosterior.GaussianNIW: Generate ramdom samples from the posterior distribution of a...

In bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

Description

Generate random samples from the posterior distribution of the following structure:

mu,Sigma|m,k,v,S \sim NIW(m,k,v,S)

x|mu,Sigma \sim Gaussian(mu,Sigma)

Where NIW() is the Normal-Inverse-Wishart distribution, Gaussian() is the Gaussian distribution. See ?dNIW and dGaussian for the definitions of these distribution.
The model structure and prior parameters are stored in a "GaussianNIW" object.
Posterior distribution is NIW(mu,Sigma|m,k,v,S).

Usage

## S3 method for class 'GaussianNIW'
rPosterior(obj, ...)

Arguments

obj	A "GaussianNIW" object.
...	Additional arguments to be passed to other inherited types.

Value

list(mu,Sigma), where mu is a numeric vector, Sigma is a symmetric positive definite matrix.

See Also

GaussianNIW, dPosterior.GaussianNIW

Examples

obj <- GaussianNIW(gamma=list(m=c(0,0),k=1,v=2,S=diag(2)))
rPosterior(obj = obj)

bbricks documentation built on July 8, 2020, 7:29 p.m.