Index: The Book of Statistical ProofsStatistical Models ▷ Univariate normal data ▷ Univariate Gaussian ▷ Maximum likelihood estimation

Theorem: Let there be a univariate Gaussian data set $y = \left\lbrace y_1, \ldots, y_n \right\rbrace$:

$\label{eq:ug} y_i \sim \mathcal{N}(\mu, \sigma^2), \quad i = 1, \ldots, n \; .$

Then, the maximum likelihood estimates for mean $\mu$ and variance $\sigma^2$ are given by

$\label{eq:ug-MLE} \begin{split} \hat{\mu} &= \frac{1}{n} \sum_{i=1}^n y_i \\ \hat{\sigma}^2 &= \frac{1}{n} \sum_{i=1}^n (y_i - \bar{y})^2 \; . \end{split}$

Proof: The likelihood function for each observation is given by the probability density function of the normal distribution

$\label{eq:ug-yi} p(y_i|\mu,\sigma^2) = \mathcal{N}(x; \mu, \sigma^2) = \frac{1}{\sqrt{2 \pi \sigma^2}} \cdot \exp \left[ -\frac{1}{2} \left( \frac{y_i-\mu}{\sigma} \right)^2 \right]$

and because observations are independent, the likelihood function for all observations is the product of the individual ones:

$\label{eq:ug-LF-s1} p(y|\mu,\sigma^2) = \prod_{i=1}^n p(y_i|\mu) = \sqrt{ \frac{1}{(2 \pi \sigma^2)^n} } \cdot \exp \left[ -\frac{1}{2} \sum_{i=1}^{n} \left( \frac{y_i-\mu}{\sigma} \right)^2 \right] \; .$

This can be developed into

$\label{eq:ug-LF-s2} \begin{split} p(y|\mu,\sigma^2) &= \left( \frac{1}{2 \pi \sigma^2} \right)^{n/2} \cdot \exp \left[ -\frac{1}{2} \sum_{i=1}^{n} \left( \frac{y_i^2 - 2 y_i \mu + \mu^2}{\sigma^2} \right) \right] \\ &= \left( \frac{1}{2 \pi \sigma^2} \right)^{n/2} \cdot \exp \left[ -\frac{1}{2 \sigma^2} \left( y^\mathrm{T} y - 2 n \bar{y} \mu + n \mu^2 \right) \right] \end{split}$

where $\bar{y} = \frac{1}{n} \sum_{i=1}^{n} y_i$ is the mean of data points and $y^\mathrm{T} y = \sum_{i=1}^{n} y_i^2$ is the sum of squared data points.

Thus, the log-likelihood function is

$\label{eq:ug-LL} \mathrm{LL}(\mu,\sigma^2) = \log p(y|\mu,\sigma^2) = -\frac{n}{2} \log (2 \pi \sigma^2) - \frac{1}{2 \sigma^2} \left( y^\mathrm{T} y - 2 n \bar{y} \mu + n \mu^2 \right) \; .$

The derivative of the log-likelihood function \eqref{eq:ug-LL} with respect to $\mu$ is

$\label{eq:dLL-dmu} \frac{\mathrm{d}\mathrm{LL}(\mu,\sigma^2)}{\mathrm{d}\mu} = \frac{n \bar{y}}{\sigma^2} - \frac{n \mu}{\sigma^2} = \frac{n}{\sigma^2} (\bar{y}-\mu)$

and setting this derivative to zero gives the MLE for $\mu$:

$\label{eq:mu-MLE} \begin{split} \frac{\mathrm{d}\mathrm{LL}(\hat{\mu},\sigma^2)}{\mathrm{d}\mu} &= 0 \\ 0 &= \frac{n}{\sigma^2} (\bar{y}-\hat{\mu}) \\ 0 &= \bar{y}-\hat{\mu} \\ \hat{\mu} &= \bar{y} \\ \hat{\mu} &= \frac{1}{n} \sum_{i=1}^n y_i \; . \end{split}$

The derivative of the log-likelihood function \eqref{eq:ug-LL} at $\hat{\mu}$ with respect to $\sigma^2$ is

$\label{eq:dLL-ds2} \begin{split} \frac{\mathrm{d}\mathrm{LL}(\hat{\mu},\sigma^2)}{\mathrm{d}\sigma^2} &= -\frac{n}{2} \frac{1}{\sigma^2} + \frac{1}{2 (\sigma^2)^2} \left( y^\mathrm{T} y - 2 n \bar{y} \hat{\mu} + n \hat{\mu}^2 \right) \\ &= -\frac{n}{2 \sigma^2} + \frac{1}{2 (\sigma^2)^2} \sum_{i=1}^n \left( y_i^2 - 2 y_i \hat{\mu} + \hat{\mu}^2 \right) \\ &= -\frac{n}{2 \sigma^2} + \frac{1}{2 (\sigma^2)^2} \sum_{i=1}^n (y_i - \hat{\mu})^2 \end{split}$

and setting this derivative to zero gives the MLE for $\sigma^2$:

$\label{eq:s2-MLE} \begin{split} \frac{\mathrm{d}\mathrm{LL}(\hat{\mu},\hat{\sigma}^2)}{\mathrm{d}\sigma^2} &= 0 \\ 0 &= \frac{1}{2 (\hat{\sigma}^2)^2} \sum_{i=1}^n (y_i - \hat{\mu})^2 \\ \frac{n}{2 \hat{\sigma}^2} &= \frac{1}{2 (\hat{\sigma}^2)^2} \sum_{i=1}^n (y_i - \hat{\mu})^2 \\ \frac{2 (\hat{\sigma}^2)^2}{n} \cdot \frac{n}{2 \hat{\sigma}^2} &= \frac{2 (\hat{\sigma}^2)^2}{n} \cdot \frac{1}{2 (\hat{\sigma}^2)^2} \sum_{i=1}^n (y_i - \hat{\mu})^2 \\ \hat{\sigma}^2 &= \frac{1}{n} \sum_{i=1}^n (y_i - \hat{\mu})^2 \\ \hat{\sigma}^2 &= \frac{1}{n} \sum_{i=1}^n (y_i - \bar{y})^2 \\ \end{split}$

Together, \eqref{eq:mu-MLE} and \eqref{eq:s2-MLE} constitute the MLE for the univariate Gaussian.

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Metadata: ID: P223 | shortcut: ug-mle | author: JoramSoch | date: 2021-04-16, 11:03.