rPosterior.GaussianNIG: 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: Generate the the density value of the posterior distribution of the following structure:

x \sim Gaussian(X beta,sigma^2)

sigma^2 \sim InvGamma(a,b)

beta \sim Gaussian(m,sigma^2 V)

Where X is a row vector, or a design matrix where each row is an obervation. InvGamma() is the Inverse-Gamma distribution, Gaussian() is the Gaussian distribution. See ?dInvGamma and dGaussian for the definitions of these distribution.
The model structure and prior parameters are stored in a "GaussianNIG" object.
Posterior distribution is the distribution of beta,sigma^2|m,V,a,b.

Usage

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

Arguments

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

Value

list(beta,sigma2), where beta is a numeric vector, sigma is a scalar value.

See Also

GaussianNIG, dPosterior.GaussianNIG

Examples

obj <- GaussianNIG(gamma=list(m=c(0,0),V=diag(2),a=1,b=1))
rPosterior(obj = obj)

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