bbricks (version 0.1.4)

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

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 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

# NOT RUN {
obj <- GaussianNIW(gamma=list(m=c(0,0),k=1,v=2,S=diag(2)))
rPosterior(obj = obj)
# }