Index: The Book of Statistical ProofsStatistical Models ▷ Univariate normal data ▷ Univariate Gaussian with known variance ▷ Posterior distribution

Theorem: Let

$\label{eq:ugkv} y = \left\lbrace y_1, \ldots, y_n \right\rbrace, \quad y_i \sim \mathcal{N}(\mu, \sigma^2), \quad i = 1, \ldots, n$

be a univariate Gaussian data set with unknown mean $\mu$ and known variance $\sigma^2$. Moreover, assume a normal distribution over the model parameter $\mu$:

$\label{eq:UGkv-prior} p(\mu) = \mathcal{N}(\mu; \mu_0, \lambda_0^{-1}) \; .$

Then, the posterior distribution is also a normal distribution

$\label{eq:UGkv-post} p(\mu|y) = \mathcal{N}(\mu; \mu_n, \lambda_n^{-1})$

and the posterior hyperparameters are given by

$\label{eq:UGkv-post-par} \begin{split} \mu_n &= \frac{\lambda_0 \mu_0 + \tau n \bar{y}}{\lambda_0 + \tau n} \\ \lambda_n &= \lambda_0 + \tau n \end{split}$

with the sample mean $\bar{y}$ and the inverse variance or precision $\tau = 1/\sigma^2$.

Proof: According to Bayes’ theorem, the posterior distribution is given by

$\label{eq:UGkv-BT} p(\mu|y) = \frac{p(y|\mu) \, p(\mu)}{p(y)} \; .$

Since $p(y)$ is just a normalization factor, the posterior is proportional to the numerator:

$\label{eq:UGkv-post-JL} p(\mu|y) \propto p(y|\mu) \, p(\mu) = p(y,\mu) \; .$

Equation \eqref{eq:ugkv} implies the following likelihood function

$\label{eq:UGkv-LF-class} \begin{split} p(y|\mu) &= \prod_{i=1}^{n} \mathcal{N}(y_i; \mu, \sigma^2) \\ &= \prod_{i=1}^{n} \frac{1}{\sqrt{2 \pi} \sigma} \cdot \exp\left[ -\frac{1}{2} \left( \frac{y_i-\mu}{\sigma} \right)^2 \right] \\ &= \left( \sqrt{\frac{1}{2 \pi \sigma^2}} \right)^n \cdot \exp\left[ -\frac{1}{2 \sigma^2} \sum_{i=1}^{n} \left( y_i-\mu \right)^2 \right] \end{split}$

which, for mathematical convenience, can also be parametrized as

$\label{eq:UGkv-LF-Bayes} \begin{split} p(y|\mu) &= \prod_{i=1}^{n} \mathcal{N}(y_i; \mu, \tau^{-1}) \\ &= \prod_{i=1}^{n} \sqrt{\frac{\tau}{2 \pi}} \cdot \exp\left[ -\frac{\tau}{2} \left( y_i-\mu \right)^2 \right] \\ &= \left( \sqrt{\frac{\tau}{2 \pi}} \right)^n \cdot \exp\left[ -\frac{\tau}{2} \sum_{i=1}^{n} \left( y_i-\mu \right)^2 \right] \end{split}$

using the inverse variance or precision $\tau = 1/\sigma^2$.

Combining the likelihood function \eqref{eq:UGkv-LF-Bayes} with the prior distribution \eqref{eq:UGkv-prior}, the joint likelihood of the model is given by

$\label{eq:UGkv-JL-s1} \begin{split} p(y,\mu) = \; & p(y|\mu) \, p(\mu) \\ = \; & \left( \sqrt{\frac{\tau}{2 \pi}} \right)^n \cdot \exp\left[ -\frac{\tau}{2} \sum_{i=1}^{n} \left( y_i-\mu \right)^2 \right] \cdot \sqrt{\frac{\lambda_0}{2 \pi}} \cdot \exp\left[ -\frac{\lambda_0}{2} \left( \mu-\mu_0 \right)^2 \right] \; . \end{split}$

Rearranging the terms, we then have:

$\label{eq:UGkv-JL-s2} p(y,\mu) = \left( \frac{\tau}{2 \pi} \right)^{n/2} \cdot \sqrt{\frac{\lambda_0}{2 \pi}} \cdot \exp\left[ -\frac{\tau}{2} \sum_{i=1}^{n} \left( y_i-\mu \right)^2 - \frac{\lambda_0}{2} \left( \mu-\mu_0 \right)^2 \right] \; .$ $\label{eq:UGkv-JL-s3} \begin{split} p(y,\mu) &= \left( \frac{\tau}{2 \pi} \right)^\frac{n}{2} \cdot \left( \frac{\lambda_0}{2 \pi} \right)^\frac{1}{2} \cdot \exp \left[ -\frac{1}{2} \left( \sum_{i=1}^n \tau (y_i-\mu)^2 + \lambda_0 (\mu-\mu_0)^2 \right) \right] \\ &= \left( \frac{\tau}{2 \pi} \right)^\frac{n}{2} \cdot \left( \frac{\lambda_0}{2 \pi} \right)^\frac{1}{2} \cdot \exp \left[ -\frac{1}{2} \left( \sum_{i=1}^n \tau (y_i^2 - 2 y_i \mu + \mu^2) + \lambda_0 (\mu^2 - 2 \mu \mu_0 + \mu_0^2) \right) \right] \\ &= \left( \frac{\tau}{2 \pi} \right)^\frac{n}{2} \cdot \left( \frac{\lambda_0}{2 \pi} \right)^\frac{1}{2} \cdot \exp \left[ -\frac{1}{2} \left( \tau (y^\mathrm{T} y - 2 n \bar{y} \mu + n \mu^2) + \lambda_0 (\mu^2 - 2 \mu \mu_0 + \mu_0^2) \right) \right] \\ &= \left( \frac{\tau}{2 \pi} \right)^\frac{n}{2} \cdot \left( \frac{\lambda_0}{2 \pi} \right)^\frac{1}{2} \cdot \exp \left[ -\frac{1}{2} \left( \mu^2 (\tau n + \lambda_0) - 2 \mu (\tau n \bar{y} + \lambda_0 \mu_0) + (\tau y^\mathrm{T} y + \lambda_0 \mu_0^2) \right) \right] \\ \end{split}$

where $\bar{y} = \frac{1}{n} \sum_{i=1}^{n} y_i$ and $y^\mathrm{T} y = \sum_{i=1}^{n} y_i^2$. Completing the square in $\mu$ then yields

$\label{eq:UGkv-JL-s4} p(y,\mu) = \left( \frac{\tau}{2 \pi} \right)^\frac{n}{2} \cdot \left( \frac{\lambda_0}{2 \pi} \right)^\frac{1}{2} \cdot \exp \left[ -\frac{\lambda_n}{2} (\mu - \mu_n)^2 + f_n \right]$

with the posterior hyperparameters

$\label{eq:UGkv-post-mu-par} \begin{split} \mu_n &= \frac{\lambda_0 \mu_0 + \tau n \bar{y}}{\lambda_0 + \tau n} \\ \lambda_n &= \lambda_0 + \tau n \end{split}$

and the remaining independent term

$\label{eq:UGkv-JL-fn} f_n = -\frac{1}{2} \left( \tau y^\mathrm{T} y + \lambda_0 \mu_0^2 - \lambda_n \mu_n^2 \right) \; .$

Ergo, the joint likelihood in \eqref{eq:UGkv-JL-s4} is proportional to

$\label{eq:UGkv-JL-s5} p(y,\mu) \propto \exp \left[ -\frac{\lambda_n}{2} (\mu - \mu_n)^2 \right] \; ,$

such that the posterior distribution over $\mu$ is given by

$\label{eq:UGkv-post-mu} p(\mu|y) = \mathcal{N}(\mu; \mu_n, \lambda_n^{-1}) \; .$

with the posterior hyperparameters given in \eqref{eq:UGkv-post-mu-par}.

Sources:

Metadata: ID: P212 | shortcut: ugkv-post | author: JoramSoch | date: 2021-03-24, 06:10.