Normal-gamma distribution

Normal-gamma distribution
parameters: \mu\, location (real)
\lambda > 0\, (real)
\alpha \ge 1\, (real)
\beta \ge 0\, (real)
support: x \in (-\infty, \infty)\,\!, \; \tau \in (0,\infty)
pdf: f(x,\tau|\mu,\lambda,\alpha,\beta) = \frac{\beta^\alpha \sqrt{\lambda}}{\Gamma(\alpha)\sqrt{2\pi}}  \, \tau^{\alpha-\frac{1}{2}}\,e^{-\beta\tau}\,e^{ -\frac{ \lambda \tau (x- \mu)^2}{2}}
mean: [1] \operatorname{E}(X)=\mu\,\! ,\quad \operatorname{E}(\Tau)= \alpha \beta^{-1}
variance: [1] \operatorname{var}(X)= \frac{\beta}{\lambda (\alpha-1)} ,\quad 
\operatorname{var}(\Tau)=\alpha \beta^{-2}

In probability theory and statistics, the normal-gamma distribution is a bivariate four-parameter family of continuous probability distributions. It is the conjugate prior of a normal distribution with unknown mean and precision.[2]




  x|\tau, \mu, \lambda \sim N(\mu,1 /(\lambda  \tau)) \,\!

has a normal distribution with mean μ and variance 1 / (λτ), where

\tau|\alpha, \beta \sim \mathrm{Gamma}(\alpha,\beta) \!

has a gamma distribution. Then (x,τ) has a normal-gamma distribution, denoted as

 (x,\tau) \sim \mathrm{NormalGamma}(\mu,\lambda,\alpha,\beta) \! .


Probability density function

f(x,\tau|\mu,\lambda,\alpha,\beta) = \frac{\beta^\alpha \sqrt{\lambda}}{\Gamma(\alpha)\sqrt{2\pi}}  \, \tau^{\alpha-\frac{1}{2}}\,e^{-\beta\tau}\,e^{ -\frac{ \lambda \tau (x- \mu)^2}{2}}



For any t > 0, tX is distributed NormalGamma(tμ,λ,α,t2β)

Marginal distributions

By construction, the marginal distribution over τ is a gamma distribution, and the conditional distribution over x given τ is a Gaussian distribution. The marginal distribution over x is a three-parameter Student's t-distribution.

Posterior distribution of the parameters

Form of the posterior for a Normal random variable with a Normal-Gamma prior:

Presume the following hierarchy for a normal random variable X with unknown mean μ and precision λ.

X & \sim \mathcal{N}(\mu, \lambda^{-1}) \\
\mu | \lambda &\sim \mathcal{N}(\mu_0, {(n_0 \lambda})^{-1}) \\
\lambda &\sim \mathcal{G}\left(\frac{\nu_0}{2},\frac{2}{S_0}\right)


μ0 is the prior mean
S0 is the prior sum of squared errors
n0 is the prior sample size
ν0 is the prior degrees of freedom

Note the joint distribution of the parameters is Normal-Gamma. The posterior distribution of the parameters can be analytically determined by Bayes' rule working with the likelihood \mathbf{L(\lambda, \mu | X)}, and the prior π(λ,μ).

\mathbf{L(\lambda, \mu | X)} & \propto \prod_{i=1}^n \lambda^{1/2} \exp[\frac{-\lambda}{2}(x_i-\mu)^2] \\ 
 &  \propto \lambda^{n/2} \exp[\frac{-\lambda}{2}\sum_{i=1}^n(x_i-\mu)^2] \\
 &  \propto \lambda^{n/2} \exp[\frac{-\lambda}{2}\sum_{i=1}^n(x_i-\bar{x} +\bar{x} -\mu)^2] \\
 &  \propto \lambda^{n/2} \exp[\frac{-\lambda}{2}\sum_{i=1}^n\left((x_i-\bar{x})^2 + (\bar{x} -\mu)^2\right)] \\
& \propto \lambda^{n/2} \exp[\frac{-\lambda}{2}\left(S + n(\bar{x} -\mu)^2\right)] 

where S=\sum_{i=1}^n(x_i-\bar{x})^2, the sum of squared errors.

Now consider the prior,

\mathbf{\pi}(\mu,\lambda)  \propto \lambda^{1/2}\exp[\frac{-\lambda n_0}{2}(\mu-\mu_0)^2] \lambda^{\frac{\nu_0}{2}-1}\exp[\frac{-\lambda S_0}{2}]

The posterior distribution of the parameters is proportional to the prior times the likelihood.

\mathbf{P(\lambda, \mu | X}) &\propto \lambda^{n/2} \exp[\frac{-\lambda}{2}\left(S + n(\bar{x} -\mu)^2\right)] 
 \lambda^{1/2}\exp[\frac{-\lambda n_0}{2}(\mu-\mu_0)^2] \lambda^{\frac{\nu_0}{2}-1}\exp[\frac{-\lambda S_0}{2}] \\ 
 &\propto \lambda^{\frac{\nu_0+n}{2}-1} \exp[\frac{-\lambda}{2}(S + S_0) ] 
 \lambda^{1/2}\exp[\frac{-\lambda}{2}\left(n_0(\mu-\mu_0)^2 + n(\bar{x} -\mu)^2\right)]  \\ 

Notice the right half begins to look like the kernel of a normal pdf and the left like a gamma. After a bit of juggling and completing the square the result will appear.


\mathbf{P(\lambda, \mu | X} )& \propto \lambda^{\frac{\nu_0+n}{2}-1} \exp[\frac{-\lambda}{2}(S + S_0) ] 
 \lambda^{1/2}\exp[\frac{- \lambda}{2} \left(n_0 (\mu^2 - 2 \mu \mu_0 + \mu_0^2 ) + n(\bar{x}^2-2 \mu \bar{x} + \mu^2)\right)]  \\ 
  & \propto \lambda^{\frac{\nu_0+n}{2}-1} \exp[\frac{-\lambda}{2}(S + S_0 + n_0 \mu_0^2 + n \bar{x}^2) ] 
 \lambda^{1/2}\exp[\frac{-\lambda}{2} (n+n_0) \left(\frac{n_0 \mu^2 + n \mu^2 }{n + n_0} - 2 \mu \frac{n\bar{x} +n_0\mu_0}{n+n_0} \right)]   \\
  & \propto \lambda^{\frac{\nu_0+n}{2}-1} \exp[\frac{-\lambda}{2}(S + S_0 + n_0 \mu_0^2 + n \bar{x}^2) ] 
 \lambda^{1/2}\exp[\frac{-\lambda}{2} (n+n_0) \left(\mu^2 - 2 \mu \frac{n\bar{x} +n_0\mu_0}{n+n_0} + \left (\frac{n\bar{x} +n_0\mu_0}{n+n_0}\right )^2 - \left (\frac{n\bar{x} +n_0\mu_0}{n+n_0}\right )^2\right)]   \\
  & \propto \lambda^{\frac{\nu_0+n}{2}-1} \exp[\frac{-\lambda}{2}\left(S + S_0 + n_0 \mu_0^2 + n \bar{x}^2 - \frac{\left (n\bar{x} +n_0\mu_0 \right )^2}{n+n_0}\right) ] 
 \lambda^{1/2}\exp[\frac{-\lambda}{2} (n+n_0) \left ( \mu - \frac{n\bar{x} +n_0\mu_0}{n+n_0}\right )^2]   \\
  & \propto \lambda^{\frac{\nu_0+n}{2}-1} \exp[\frac{-\lambda}{2}\left(S + S_0 + \frac{nn_0 (\bar{x}-\mu_0)^2}{n+n_0}\right) ] 
 \lambda^{1/2}\exp[\frac{-\lambda}{2} (n+n_0) \left ( \mu - \frac{n\bar{x} +n_0\mu_0}{n+n_0}\right )^2]   .\\


This is a normal gamma pdf with parameters 
\mathcal{NG} \left(\frac{n\bar{x} +n_0\mu_0}{n+n_0}, n+n_0, \frac{\nu_0+n}{2}, 2\left(S + S_0 + \frac{nn_0 (\bar{x}-\mu_0)^2}{n+n_0}\right)^{-1} \right) .

The reference prior is[citation needed] the limiting case as

n_0, S_0, \mu_0 \rightharpoonup 0

and \nu_0 \rightharpoonup -1

Generating normal-gamma random variates

Generation of random variates is straightforward:

  1. Sample τ from a gamma distribution with parameters α and β
  2. Sample x from a normal distribution with mean μ and variance 1 / (λτ)

Related distributions


  1. ^ a b Bernardo & Smith (1993, p.434)
  2. ^ Bernardo & Smith (1993, pages 136, 268, 434)


  • Bernardo, J.M.; Smith, A.F.M. (1993) Bayesian Theory, Wiley. ISBN 0-471-49464-X
  • Dearden et al. Bayesian Q-learning, Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), July 26–30, 1998, Madison, Wisconsin, USA.

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