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Milds # Normal-Wishart distribution

Notation $({\boldsymbol {\mu }},{\boldsymbol {\Lambda }})\sim \mathrm {NW} ({\boldsymbol {\mu }}_{0},\lambda ,\mathbf {W} ,\nu )$ ${\boldsymbol {\mu }}_{0}\in \mathbb {R} ^{D}\,$ location (vector of real)$\lambda >0\,$ (real)$\mathbf {W} \in \mathbb {R} ^{D\times D}$ scale matrix (pos. def.)$\nu >D-1\,$ (real) ${\boldsymbol {\mu }}\in \mathbb {R} ^{D};{\boldsymbol {\Lambda }}\in \mathbb {R} ^{D\times D}$ covariance matrix (pos. def.) $f({\boldsymbol {\mu }},{\boldsymbol {\Lambda }}|{\boldsymbol {\mu }}_{0},\lambda ,\mathbf {W} ,\nu )={\mathcal {N}}({\boldsymbol {\mu }}|{\boldsymbol {\mu }}_{0},(\lambda {\boldsymbol {\Lambda }})^{-1})\ {\mathcal {W}}({\boldsymbol {\Lambda }}|\mathbf {W} ,\nu )$ In probability theory and statistics, the normal-Wishart distribution (or Gaussian-Wishart distribution) is a multivariate four-parameter family of continuous probability distributions. It is the conjugate prior of a multivariate normal distribution with unknown mean and precision matrix (the inverse of the covariance matrix).

## Definition

Suppose

${\boldsymbol {\mu }}|{\boldsymbol {\mu }}_{0},\lambda ,{\boldsymbol {\Lambda }}\sim {\mathcal {N}}({\boldsymbol {\mu }}_{0},(\lambda {\boldsymbol {\Lambda }})^{-1})$ has a multivariate normal distribution with mean ${\boldsymbol {\mu }}_{0}$ and covariance matrix $(\lambda {\boldsymbol {\Lambda }})^{-1}$ , where

${\boldsymbol {\Lambda }}|\mathbf {W} ,\nu \sim {\mathcal {W}}({\boldsymbol {\Lambda }}|\mathbf {W} ,\nu )$ has a Wishart distribution. Then $({\boldsymbol {\mu }},{\boldsymbol {\Lambda }})$ has a normal-Wishart distribution, denoted as

$({\boldsymbol {\mu }},{\boldsymbol {\Lambda }})\sim \mathrm {NW} ({\boldsymbol {\mu }}_{0},\lambda ,\mathbf {W} ,\nu ).$ ## Characterization

### Probability density function

$f({\boldsymbol {\mu }},{\boldsymbol {\Lambda }}|{\boldsymbol {\mu }}_{0},\lambda ,\mathbf {W} ,\nu )={\mathcal {N}}({\boldsymbol {\mu }}|{\boldsymbol {\mu }}_{0},(\lambda {\boldsymbol {\Lambda }})^{-1})\ {\mathcal {W}}({\boldsymbol {\Lambda }}|\mathbf {W} ,\nu )$ ## Properties

### Marginal distributions

By construction, the marginal distribution over ${\boldsymbol {\Lambda }}$ is a Wishart distribution, and the conditional distribution over ${\boldsymbol {\mu }}$ given ${\boldsymbol {\Lambda }}$ is a multivariate normal distribution. The marginal distribution over ${\boldsymbol {\mu }}$ is a multivariate t-distribution.

## Posterior distribution of the parameters

After making $n$ observations ${\boldsymbol {x}}_{1},\dots ,{\boldsymbol {x}}_{n}$ , the posterior distribution of the parameters is

$({\boldsymbol {\mu }},{\boldsymbol {\Lambda }})\sim \mathrm {NW} ({\boldsymbol {\mu }}_{n},\lambda _{n},\mathbf {W} _{n},\nu _{n}),$ where

$\lambda _{n}=\lambda +n,$ ${\boldsymbol {\mu }}_{n}={\frac {\lambda {\boldsymbol {\mu }}_{0}+n{\boldsymbol {\bar {x}}}}{\lambda +n}},$ $\nu _{n}=\nu +n,$ $\mathbf {W} _{n}^{-1}=\mathbf {W} ^{-1}+\sum _{i=1}^{n}({\boldsymbol {x}}_{i}-{\boldsymbol {\bar {x}}})({\boldsymbol {x}}_{i}-{\boldsymbol {\bar {x}}})^{T}+{\frac {n\lambda }{n+\lambda }}({\boldsymbol {\bar {x}}}-{\boldsymbol {\mu }}_{0})({\boldsymbol {\bar {x}}}-{\boldsymbol {\mu }}_{0})^{T}.$ ## Generating normal-Wishart random variates

Generation of random variates is straightforward:

1. Sample ${\boldsymbol {\Lambda }}$ from a Wishart distribution with parameters $\mathbf {W}$ and $\nu$ 2. Sample ${\boldsymbol {\mu }}$ from a multivariate normal distribution with mean ${\boldsymbol {\mu }}_{0}$ and variance $(\lambda {\boldsymbol {\Lambda }})^{-1}$ ## Related distributions

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