# rPosteriorPredictive.GaussianNIW: Generate random samples from the posterior predictive... In bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

## Description

Generate random samples from the posterior predictive 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 predictive is a distribution of x|m,k,v,S.

## Usage

 ```1 2``` ```## S3 method for class 'GaussianNIW' rPosteriorPredictive(obj, n, ...) ```

## Arguments

 `obj` A "GaussianNIW" object. `n` integer, number of samples. `...` Additional arguments to be passed to other inherited types.

## Value

A matrix of n rows, each row is a sample.

## References

Murphy, Kevin P. "Conjugate Bayesian analysis of the Gaussian distribution." def 1.22 (2007): 16.

Gelman, Andrew, et al. "Bayesian Data Analysis Chapman & Hall." CRC Texts in Statistical Science (2004).

`GaussianNIW`, `dPosteriorPredictive.GaussianNIW`
 ```1 2``` ```obj <- GaussianNIW(gamma=list(m=c(0,0),k=1,v=2,S=diag(2))) rPosteriorPredictive(obj=obj,20) ```