rPosterior.GaussianInvWishart: Generate one ramdom sample from the posterior distribution of...

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

Description

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

x \sim Gaussian(mu,Sigma)

Sigma \sim InvWishart(v,S)

mu is known. Gaussian() is the Gaussian distribution. See ?dGaussian and ?dInvWishart for the definition of the distributions.
The model structure and prior parameters are stored in a "GaussianInvWishart" object.
Posterior distribution is InvWishart(Sigma|v,S).

Usage

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

Arguments

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

Value

matrix, a sample of Sigma.

See Also

GaussianInvWishart, dPosterior.GaussianInvWishart

Examples

obj <- GaussianInvWishart(gamma=list(mu=c(-1.5,1.5),v=3,S=diag(2)))
rPosterior(obj = obj)

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