# Minimum variance and maximum likelihood estimation Part II

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## Outline

• The following topics will be covered in this lecture:
• A simple example of maximum likelihood estimation
• Bayesian maximum a posteriori estimation

## Motivation

• In the last lecture, we saw that when our modeled random state $$\pmb{x}$$ and some observed piece of data $$\pmb{y}$$ are jointly Gaussian, the conditional Gaussian mean is precisely the BLUE.

• The BLUE and its covariance parameterized the conditional Gaussian distribution for $$\pmb{x}|\pmb{y}$$.

• The Gauss-Markov theorem does not require that the underlying distributions are actually Gaussian, however.
• Without the Gaussian assumption, we can still construct the BLUE and its covariance as discussed already, though it will not generally parameterize the conditional distribution for $$\pmb{x}|\pmb{y}$$, which may be non-Gaussian.
• However, when the underlying distributions are Gaussian, as above, we also get the equivalence of the conditional mean as the maximum likelihood estimator.

• To explain this notion, we must first introduce the idea of a likelihood function.

Likelihood function
Let $$p_{\pmb{\theta}}(\pmb{x})$$ be a probability density that depends on the parameter vector $$\pmb{\theta}$$ as a free variable. If $$\pmb{x}_0$$ is an observed realization of the random variable, then we denote the likelihood function \begin{align} L_{\pmb{x}_0}(\pmb{\theta}):= p_{\pmb{\theta}}(\pmb{x}_0), \end{align} i.e., we evaluate the density for $$\pmb{x}_0$$ with respect to the particular choice of $$\pmb{\theta}$$ as a free variable.
• The definition above simply re-arranges the terms for the density, and which variable we treat as the argument.

• This provides a means, for an unknown value of the free parameter $$\pmb{\theta}$$, to consider which form of the density best matches the observed data.

## Maximum likelihood estimation

• If we suppose, furthermore, we have a random sample $$\pmb{x}_{k:0}$$, independently and identically distributed according to the parent distribution for some unknown choice of $$\pmb{\theta}$$;

• the joint likelihood for the random sample is given by

\begin{align} L_{\pmb{x}_{k:0}}(\pmb{\theta})&:= p_{\pmb{\theta}}(\pmb{x}_{k:0})\\ &=\prod_{i=0}^k p_{\pmb{\theta}}(\pmb{x}_i), \end{align} due to independence.

• Another way in which we might thus consider an estimate “optimal” is if it maximizes the joint likelihood of our observed data:

Maximum likelihood estimation
Let $$\hat{\pmb{\Theta}}$$ be a point estimator for an unknown parameter $$\pmb{\theta}$$, depending on the random sample $$\pmb{x}_{k:0}$$. We say that $$\hat{\pmb{\Theta}}$$ is a maximum likelihood estimator of $$\theta$$ if for any other point estimator $$\tilde{\pmb{\Theta}}$$, \begin{align} L_{\pmb{x}_{k:0}}\left(\tilde{\pmb{\Theta}}\right)\leq L_{\pmb{x}_{k:0}}\left(\hat{\pmb{\Theta}}\right), \end{align} i.e., for any particular realization $$\hat{\pmb{\theta}}$$ of the random variable $$\hat{\pmb{\Theta}}$$ depending on the outcome of the random sample $$\pmb{x}_{k:0}$$, $$\hat{\pmb{\theta}}$$ is the value that maximizes the joint density for $$\pmb{x}_{k:0}$$.

### Maximum likelihood estimation

• It is important to recognize that the joint likelihood,

\begin{align} L_{\pmb{x}_{k:0}}(\pmb{\theta}) &=\prod_{i=0}^k p_{\pmb{\theta}}(\pmb{x}_i), \end{align} can rarely be solved analytically.

• However, we note that $$\log$$ is monotonic, such that an increase in the argument uniformly corresponds to an increase in the output.

• It is equivalent, then, to maximize the log-likelihood, defined

\begin{align} \mathcal{l}_{\pmb{x}_{k:0}}(\pmb{\theta})&:= \log\left(\prod_{i=0}^k p_{\pmb{\theta}}(\pmb{x}_i)\right) \\ &=\sum_{i=0}^k \log\left(p_{\pmb{\theta}}(\pmb{x}_i) \right). \end{align}

• Furthermore, writing the minus-log-likelihood,

\begin{align} \mathcal{J}(\pmb{\theta}):= - \mathcal{l}_{\pmb{x}_{k:0}}(\pmb{\theta}) =-\sum_{i=0}^k \log\left(p_{\pmb{\theta}}(\pmb{x}_i) \right) \end{align} finding the maximum likelihood estimate is equivalent to an objective function minimization problem in optimization.

### Maximum likelihood estimation

• Let's consider again the simple example of estimating the fixed, true temperature $$T_t$$ from two random observations,

\begin{align} T_1 = T_t + \epsilon_1 & &\epsilon_1 \sim N\left(0, \sigma_1^2\right) \\ T_2 = T_t + \epsilon_2 & & \epsilon_2 \sim N\left(0, \sigma_2^2\right) \end{align}

• The probability density of an observation $$T_i$$ given the true value $$T_t$$ and the standard deviation $$\sigma_i$$ is given as

\begin{align} p_{\sigma_i, T_t}(T_i) = \frac{1}{\sqrt{2\pi}\sigma_i}e^{-\frac{\left(T_i - T_t\right)^2}{2\sigma_i^2}} \end{align}

• This corresponds then to saying the likelihood of the true value $$T_t$$ is given the observed $$T_i$$ is $$L_{\sigma_i,T_i}(T) = P_{\sigma_i,T_t}\left( T_i\right)$$.

• If we take the minus-log-likelihood, we say this is equal to

\begin{align} \mathcal{J}(T) =\text{constants} + \frac{1}{2}\left[\frac{\left(T -T_1\right)^2}{\sigma_1^2} + \frac{\left(T -T_2\right)^2}{\sigma_2^2} \right], \end{align} where “constants” refer to terms that do not involve the free variable $$T$$.

• We write the minus-log-likelihood this way, because the constant terms have no bearing on which choice of $$T$$ minimizes the above objective function.

• Rather, we see this as a penalty function given in terms of the square deviation of $$T$$ from the observations, proportional to the observation precisions.

### Maximum likelihood estimation

• Taking the derivative with respect to $$T$$, this equals zero precisely where

\begin{align} &0 = \frac{T-T_1}{\sigma_1^2} + \frac{T-T_2}{\sigma_2^2} \\ \Leftrightarrow & T= \frac{T_1 \sigma_2^2}{\sigma_1^2 + \sigma_2^2} + \frac{T_2 \sigma_1^2}{\sigma_1^2 + \sigma_2^2} \end{align} as with the minimum variance estimator.

• This is actually a general property for Gaussian distributions.

• This is due to the geometry of the Gaussian exactly, in that its density is unimodally peaked at the mean, with symmetry about this value.

• This means that the mean of the Gaussian and the mode (the density maximizing value) always coincide.

• In this simple example, we again assumed that $$T_t$$ was a fixed, unknown value;

• however, we want to consider again the case where $$\pmb{x}$$ is actually a random variable due to uncertain initial data and evolution.
• If we generally suppose that we have a joint density for $$\pmb{x}$$ and $$\pmb{y}$$, we can instead write this as a case of Bayesian maximum a posteriori estimation.

## Maximum a posteriori estimation

• Let's consider the relationship of conditional probability, supposing we have a joint density on the vectors $$\pmb{x}$$ and $$\pmb{y}$$,

\begin{align} p(\pmb{x},\pmb{y})& = p(\pmb{x}|\pmb{y}) p(\pmb{y}), \\ p(\pmb{x},\pmb{y})&= p(\pmb{y}|\pmb{x}) p(\pmb{x}), \end{align}

• which together give Bayes' law as follows.

\begin{align} p(\pmb{x}|\pmb{y}) = \frac{p(\pmb{y}|\pmb{x}) p(\pmb{x})}{p(\pmb{y})}. \end{align}

• Viewing this like maximum likelihood estimation, we can find the value $$\hat{\pmb{x}}$$ that maximizes the conditional density for $$\pmb{x}|\pmb{y}$$.

• Therefore, up to proportionality, we say

\begin{align} p(\pmb{x}|\pmb{y})\propto p(\pmb{y}|\pmb{x}) p(\pmb{x}); \end{align} where

• $$p(\pmb{x}|\pmb{y})$$ is known as the posterior;
• $$p(\pmb{y}|\pmb{x})$$ is known as the likelihood of the data; and
• $$p(\pmb{x})$$ is the prior knowledge of $$\pmb{x}$$.
• Particularly, it is thus sufficient to maximize the product of the likelihood and the prior to find $$\hat{\pmb{x}}$$ that maximizes the posterior.

• Again, the marginal density $$p(\pmb{y})$$ for the observed data makes no difference in the maximal solution with respect to $$\pmb{x}$$.

### Maximum a posteriori estimation

• We recall that if $$p(\pmb{x},\pmb{y})$$ is jointly Gaussian, then the posterior $$p(\pmb{x}|\pmb{y})$$ is also Gaussian.

• Therefore, the conditional Gaussian mean is both the minimum variance and maximum a posteriori estimator.

• Particularly, if we recall that for jointly Gaussian distributed variables,

\begin{align} \begin{pmatrix} \pmb{x} \\ \pmb{y} \end{pmatrix} \sim N\left(\begin{pmatrix}\overline{\pmb{x}} \\ \overline{\pmb{y}}\end{pmatrix}, \begin{pmatrix} \boldsymbol{\Sigma}_{\pmb{x}} & \boldsymbol{\Sigma}_{\pmb{x},\pmb{y}} \\ \boldsymbol{\Sigma}_{\pmb{x},\pmb{y}} & \boldsymbol{\Sigma}_{\pmb{y}} \end{pmatrix} \right), \end{align}

• the posterior for $$\pmb{x}$$ given a particular realization of $$\pmb{y}$$ is given by the conditional density, proportional to

\begin{align} p(\pmb{x}|\pmb{y}) \propto \exp\left\{-\frac{1}{2}\parallel \pmb{x} - \overline{\pmb{x}} - \boldsymbol{\Sigma}_{\pmb{x},\pmb{y}}\boldsymbol{\Sigma}_{\pmb{y}}^{-1}\pmb{\delta}_{\pmb{y}}\parallel_{\boldsymbol{\Sigma}_{\pmb{x}} - \boldsymbol{\Sigma}_{\pmb{x},\pmb{y}} \boldsymbol{\Sigma}_{\pmb{y}}^{-1} \boldsymbol{\Sigma}_{\pmb{x},\pmb{y}}}^2\right\}. \end{align}

• Because the above is a hyper-exponential penalty function, for how far $$\pmb{x}$$ lies away from the conditional mean, this is clearly maximized at the condtional mean.

• However, the conditional mean is not always the maximum a posteriori estimator for a generic density.

• Nonetheless, the Bayesian proportionality statement

\begin{align} p(\pmb{x}|\pmb{y}) \propto p(\pmb{y}|\pmb{x}) p(\pmb{x}) \end{align} gives a very flexible means to construct a Bayesian maximum a posteriori estimator.

• Taking the minus-log-likelihood once again, we attain a more general objective function minimization problem.