# 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

 ```1 2``` ```## 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.

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