# rNIW: Random number generation for Normal-Inverse-Wishart (NIW)... In bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

## Description

Generate a NIW sample.For a random vector mu, and a random matrix Sigma, the density function is defined as:

sqrt(2 pi^p |Sigma/k|)^{-1} exp(-1/2 (mu-m )^T (Sigma/k)^{-1} (mu-m)) (2^{(v p)/2} Gamma_p(v/2) |S|^{-v/2})^{-1} |Sigma|^{(-v-p-1)/2} exp(-1/2 tr(Sigma^{-1} S))

Where p is the dimension of mu and Sigma.

## Usage

 `1` ```rNIW(m, k, v, S) ```

## Arguments

 `m` numeric, mean of mu. `k` numeric, precision of mu. `v` numeric, degree of freedom of Sigma. `S` numeric, a symmetric positive definite scale matrix of Sigma, S is proportional to E(Sigma).

## Value

A list of two list(mu,Sigma), where 'mu' is a numeric vector, the Gaussian sample; 'Sigma' is a symmetric positive definite matrix, the Inverse-Wishart sample.

## References

O'Hagan, Anthony, and Jonathan J. Forster. Kendall's advanced theory of statistics, volume 2B: Bayesian inference. Vol. 2. Arnold, 2004.

MARolA, K. V., JT KBNT, and J. M. Bibly. Multivariate analysis. AcadeInic Press, Londres, 1979.

`dNIW`
 `1` ```rNIW(m=runif(3),k=0.001,v=5,S=crossprod(matrix(rnorm(15),5,3))) ```