# Normal-inverse-gamma distribution

Parameters $\mu\,$ location (real) $\lambda > 0\,$ (real) $\alpha > 0\,$ (real) $\beta > 0\,$ (real) $x \in (-\infty, \infty)\,\!, \; \sigma^2 \in (0,\infty)$ $\frac {\sqrt{\lambda}} {\sigma\sqrt{2\pi} } \frac{\beta^\alpha}{\Gamma(\alpha)} \, \left( \frac{1}{\sigma^2} \right)^{\alpha + 1} e^{ -\frac { 2\beta + \lambda(x - \mu)^2} {2\sigma^2} }$

In probability theory and statistics, the normal-inverse-gamma distribution (or Gaussian-inverse-gamma distribution) is a four-parameter family of multivariate continuous probability distributions. It is the conjugate prior of a normal distribution with unknown mean and variance.

## Definition

Suppose

$x | \sigma^2, \mu, \lambda\sim \mathrm{N}(\mu,\sigma^2 / \lambda) \,\!$

has a normal distribution with mean $\mu$ and variance $\sigma^2 / \lambda$, where

$\sigma^2|\alpha, \beta \sim \Gamma^{-1}(\alpha,\beta) \!$

has an inverse gamma distribution. Then $(x,\sigma^2)$ has a normal-inverse-gamma distribution, denoted as

$(x,\sigma^2) \sim \text{N-}\Gamma^{-1}(\mu,\lambda,\alpha,\beta) \! .$

($\text{NIG}$ is also used instead of $\text{N-}\Gamma^{-1}.$)

In a multivariate form of the normal-inverse-gamma distribution, $\mathbf{x} | \sigma^2, \boldsymbol{\mu}, \mathbf{V}^{-1}\sim \mathrm{N}(\boldsymbol{\mu},\sigma^2 \mathbf{V}) \,\!$ -- that is, conditional on $\sigma^2$, $\mathbf{x}$ is a $k \times 1$ random vector that follows the multivariate normal distribution with mean $\boldsymbol{\mu}$ and covariance $\sigma^2\mathbf{V}$ -- while, as in the univariate case, $\sigma^2|\alpha, \beta \sim \Gamma^{-1}(\alpha,\beta) \!$.

## Characterization

### Probability density function

$f(x,\sigma^2|\mu,\lambda,\alpha,\beta) = \frac {\sqrt{\lambda}} {\sigma\sqrt{2\pi} } \, \frac{\beta^\alpha}{\Gamma(\alpha)} \, \left( \frac{1}{\sigma^2} \right)^{\alpha + 1} \exp \left( -\frac { 2\beta + \lambda(x - \mu)^2} {2\sigma^2} \right)$

For the multivariate form where $\mathbf{x}$ is a $k \times 1$ random vector,

$f(\mathbf{x},\sigma^2|\mu,\mathbf{V}^{-1},\alpha,\beta) = |\mathbf{V}|^{-1/2} {(2\pi)^{-k/2} } \, \frac{\beta^\alpha}{\Gamma(\alpha)} \, \left( \frac{1}{\sigma^2} \right)^{k/2 + \alpha + 1} \exp \left( -\frac { 2\beta + (\mathbf{x} - \boldsymbol{\mu})' \mathbf{V}^{-1} (\mathbf{x} - \boldsymbol{\mu})} {2\sigma^2} \right).$

where $|\mathbf{V}|$ is the determinant of the $k \times k$ matrix $\mathbf{V}$. Note how this last equation reduces to the first form if $k = 1$ so that $\mathbf{x}, \mathbf{V}, \boldsymbol{\mu}$ are scalars.

#### Alternative parameterization

It is also possible to let $\gamma = 1 / \lambda$ in which case the pdf becomes

$f(x,\sigma^2|\mu,\gamma,\alpha,\beta) = \frac {1} {\sigma\sqrt{2\pi\gamma} } \, \frac{\beta^\alpha}{\Gamma(\alpha)} \, \left( \frac{1}{\sigma^2} \right)^{\alpha + 1} \exp \left( -\frac{2\gamma\beta + (x - \mu)^2}{2\gamma \sigma^2} \right)$

In the multivariate form, the corresponding change would be to regard the covariance matrix $\mathbf{V}$ instead of its inverse $\mathbf{V}^{-1}$ as a parameter.

### Cumulative distribution function

$F(x,\sigma^2|\mu,\lambda,\alpha,\beta) = \frac{e^{-\frac{\beta }{\sigma ^2}} \left(\frac{\beta }{\sigma ^2}\right)^{\alpha } \left(\text{erf}\left(\frac{\sqrt{\lambda } (x-\mu )}{\sqrt{2} \sigma }\right)+1\right)}{2 \sigma ^2 \Gamma (\alpha )}$

### Differential equation

The probability density function of the normal-inverse-gamma distribution is a solution to the following differential equation:

$\left\{\begin{array}{l} \sigma ^2 f'(x)+\lambda f(x) (x-\mu )=0, \\ f(0)=\frac{\sqrt{\lambda} \beta ^{\alpha} \left(\frac{1}{\sigma ^2}\right)^{\alpha +1} e^{\frac{-2 \beta -\lambda \mu ^2}{2 \sigma^2}}}{\sqrt{2 \pi} \sigma \Gamma (\alpha )} \end{array}\right\}$

## Properties

### Marginal distributions

Given $(x,\sigma^2) \sim \text{N-}\Gamma^{-1}(\mu,\lambda,\alpha,\beta) \! .$ as above, $\sigma^2$ by itself follows an inverse gamma distribution:

$\sigma^2 \sim \Gamma^{-1}(\alpha,\beta) \!$

while $\sqrt{\frac{\alpha\lambda}{\beta}} (x - \mu)$ follows a t distribution with $2 \alpha$ degrees of freedom.

In the multivariate case, the marginal distribution of $\mathbf{x}$ is a multivariate t distribution:

$\mathbf{x} \sim t_{2\alpha}(\boldsymbol{\mu}, \frac{\beta}{\alpha} \mathbf{V}) \!$

## Posterior distribution of the parameters

See the articles on normal-gamma distribution and conjugate prior.

## Interpretation of the parameters

See the articles on normal-gamma distribution and conjugate prior.

## Generating normal-inverse-gamma random variates

Generation of random variates is straightforward:

1. Sample $\sigma^2$ from an inverse gamma distribution with parameters $\alpha$ and $\beta$
2. Sample $x$ from a normal distribution with mean $\mu$ and variance $\sigma^2/\lambda$

## Related distributions

• The normal-gamma distribution is the same distribution parameterized by precision rather than variance
• A generalization of this distribution which allows for a multivariate mean and a completely unknown positive-definite covariance matrix $\sigma^2 \mathbf{V}$ (whereas in the multivariate inverse-gamma distribution the covariance matrix is regarded as known up to the scale factor $\sigma^2$) is the normal-inverse-Wishart distribution

## References

• Denison, David G. T. ; Holmes, Christopher C.; Mallick, Bani K.; Smith, Adrian F. M. (2002) Bayesian Methods for Nonlinear Classification and Regression, Wiley. ISBN 0471490369
• Koch, Karl-Rudolf (2007) Introduction to Bayesian Statistics (2nd Edition), Springer. ISBN 354072723X