# dist.Normal.Inverse.Wishart: Normal-Inverse-Wishart Distribution In LaplacesDemon: Complete Environment for Bayesian Inference

## Description

These functions provide the density and random number generation for the normal-inverse-Wishart distribution.

## Usage

 ```1 2``` ```dnorminvwishart(mu, mu0, lambda, Sigma, S, nu, log=FALSE) rnorminvwishart(n=1, mu0, lambda, S, nu) ```

## Arguments

 `mu` This is data or parameters in the form of a vector of length k or a matrix with k columns. `mu0` This is mean vector mu[0] with length k or matrix with k columns. `lambda` This is a positive-only scalar. `n` This is the number of random draws. `nu` This is the scalar degrees of freedom nu. `Sigma` This is a k x k covariance matrix Sigma. `S` This is the symmetric, positive-semidefinite, k x k scale matrix S. `log` Logical. If `log=TRUE`, then the logarithm of the density is returned.

## Details

• Application: Continuous Multivariate

• Density: p(mu, Sigma) = N(mu | mu[0], (1/lambda) Sigma) W^(-1)(Sigma | nu, S)

• Inventors: Unknown

• Notation 1: (mu, Sigmaa) ~ NIW(mu[0], lambda, S, nu)

• Notation 2: p(mu, Sigma) = NIW(mu, Sigma | mu[0], lambda, S, nu)

• Parameter 1: location vector mu[0]

• Parameter 2: lambda > 0

• Parameter 3: symmetric, positive-semidefinite k x k scale matrix S

• Parameter 4: degrees of freedom nu >= k

• Mean: Unknown

• Variance: Unknown

• Mode: Unknown

The normal-inverse-Wishart distribution, or Gaussian-inverse-Wishart distribution, is a multivariate four-parameter continuous probability distribution. It is the conjugate prior of a multivariate normal distribution with unknown mean and covariance matrix.

## Value

`dnorminvwishart` gives the density and `rnorminvwishart` generates random deviates and returns a list with two components.

## Author(s)

Statisticat, LLC. [email protected]

`dmvn` and `dinvwishart`.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```library(LaplacesDemon) K <- 3 mu <- rnorm(K) mu0 <- rnorm(K) nu <- K + 1 S <- diag(K) lambda <- runif(1) #Real scalar Sigma <- as.positive.definite(matrix(rnorm(K^2),K,K)) x <- dnorminvwishart(mu, mu0, lambda, Sigma, S, nu, log=TRUE) out <- rnorminvwishart(n=10, mu0, lambda, S, nu) joint.density.plot(out\$mu[,1], out\$mu[,2], color=TRUE) ```

### Example output

```
```

LaplacesDemon documentation built on July 1, 2018, 9:02 a.m.