ECE662: Statistical Pattern Recognition and Decision Making Processes

Spring 2008, Prof. Boutin

Collectively created by the students in the class

# Lecture 7 Lecture notes

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## Lecture Content

• Maximum Likelihood Estimation and Bayesian Parameter Estimation
• Parametric Estimation of Class Conditional Density

The class conditional density $p(\vec{x}|w_i)$ can be estimated using training data. We denote the parameter of estimation as $\vec{\theta}$. There are two methods of estimation discussed.

## Maximum Likelihood Estimation

Let "c" denote the number of classes. D, the entire collection of sample data. $D_1, \ldots, D_c$ represent the classification of data into classes $\omega_1, \ldots, \omega_c$. It is assumed that: - Samples in $D_i$ give no information about the samples in $D_j, i \neq j$, and - Each sample is drawn independently.

Example: The class conditional density $p(\vec{x}|w_i)$ depends on parameter $\vec{\theta_i}$. If $X ~ N(\mu,\sigma^2)$ denotes the class conditional density; then $\vec{\theta}=[\mu,\sigma^2]$.

Let n be the size of training sample, and $D=\{\vec{X_1}, \ldots, \vec{X_n}\}$. Then,

$p(\vec{X}|\omega_i,\vec{\theta_i})$ equals $p(\vec{X}|\vec{\theta})$ for a single class.

The Likelihood Function is, then, defined as $p(D|\vec{\theta})=\displaystyle \prod_{k=1}^n p(\vec{X_k}|\vec{\theta})$, which needs to be maximized for obtaining the parameter.

Since logarithm is a monotonic function, maximizing the Likelihood is same as maximizing log of Likelihood which is defined as $l(\vec{\theta})=log p(D|\vec{\theta})=\displaystyle log(\prod_{k=1}^n p(\vec{X_k}|\vec{\theta}))=\displaystyle \sum_{k=1}^n log(p(\vec{X_k}|\vec{\theta}))$.

"l" is the log likelihood function.

Maximize log likelyhood function with respect to $\vec{\theta}$

$\rightarrow \hat{\theta} = argmax \left( l (\vec{\theta}) \right)$

If $l(\vec{\theta})$ is a differentiable function

Let $\vec{\theta} = \left[ \theta_1, \theta_2, \cdots , \theta_p \right]$ be 1 by p vector, then

$\nabla_{\vec{\theta}} = \left[ \frac{\partial}{\partial\theta_1} \frac{\partial}{\partial\theta_2} \cdots \frac{\partial}{\partial\theta_p} \right]^{t}$

Then, we can compute the first derivatives of log likelyhood function,

$\rightarrow \nabla_{\vec{\theta}} ( l (\vec{\theta}) ) = \sum_{k=1}^{n} \nabla_{\vec{\theta}} \left[ log(p(\vec{x_k} | \vec{\theta})) \right]$

and equate this first derivative to be zero

$\rightarrow \nabla_{\vec{\theta}} ( l (\vec{\theta}) ) = 0$

## Example of Guassian case

Assume that covariance matrix are known.

$p(\vec{x_k} | \vec{\mu}) = \frac{1}{ \left( (2\pi)^{d} |\Sigma| \right)^{\frac{1}{2}}} exp \left[ - \frac{1}{2} (\vec{x_k} - \vec{\mu})^{t} \Sigma^{-1} (\vec{x_k} - \vec{\mu}) \right]$

Step 1: Take log

$log p(\vec{x_k} | \vec{\mu}) = -\frac{1}{2} log \left( (2\pi)^d |\Sigma| \right) - \frac{1}{2} (\vec{x_k} - \vec{\mu})^{t} \Sigma^{-1} (\vec{x_k} - \vec{\mu})$

Step 2: Take derivative

$\frac{\partial}{\partial\vec{\mu}} \left( log p(\vec{x_k} | \vec{\mu}) \right) = \frac{1}{2} \left[ (\vec{x_k} - \vec{\mu})^t \Sigma^{-1}\right]^t + \frac{1}{2} \left[ \Sigma^{-1} (\vec{x_k} - \vec{\mu}) \right] = \Sigma^{-1} (\vec{x_k} - \vec{\mu})$

Step 3: Equate to 0

$\sum_{k=1}^{n} \Sigma^{-1} (\vec{x_k} - \vec{\mu}) = 0$

$\rightarrow \Sigma^{-1} \sum_{k=1}^{n} (\vec{x_k} - \vec{\mu}) = 0$

$\rightarrow \Sigma^{-1} \left[ \sum_{k=1}^{n} \vec{x_k} - n \vec{\mu}\right] = 0$

$\Longrightarrow \hat{\vec{\mu}} = \frac{1}{n} \sum_{k=1}^{n} \vec{x_k}$

This is the sample mean for a sample size n.

• Simple
• Converges
• Asymptotically unbiased (though biased for small N)

## Bayesian Parameter Estimation

For a given class, let $x$ be feature vector of the class and $\theta$ be parameter of pdf of $x$ to be estimated.

And let $D= \{ x_1, x_2, \cdots, x_n \}$ , where $x$ are training samples of the class

Note that $\theta$ is random variable with probability density $p(\theta)$

where

Here is a good example .

## EXAMPLE: Bayesian Inference for Gaussian Mean

The univariate case. The variance is assumed to be known.

Here's a summary of results:

• Univariate Gaussian density $p(x|\mu)\sim N(\mu,\sigma^{2})$
• Prior density of the mean $p(\mu)\sim N(\mu_{0},\sigma_{0}^{2})$
• Posterior density of the mean $p(\mu|D)\sim N(\mu_{n},\sigma_{n}^{2})$

where

• $\mu_{n}=\left(\frac{n\sigma_{0}^{2}}{n\sigma_{0}^{2}+\sigma^{2}}\right)\hat{\mu}_{n}+\frac{\sigma^{2}}{n\sigma_{0}^{2}+\sigma^{2}}\mu_{0}$
• $\sigma_{n}^{2}=\frac{\sigma_{0}^{2}\sigma^{2}}{n\sigma_{0}^{2}+\sigma^{2}}$
• $\hat{\mu}_{n}=\frac{1}{n}\sum_{k=1}^{n}x_{k}$

Finally, the class conditional density is given by $p(x|D)\sim N(\mu_{n},\sigma^{2}+\sigma_{n}^{2})$

The above formulas can be interpreted as: in making prediction for a single new observation, the variance of the estimate will have two components: 1) $\sigma^{2}$ - the inherent variance within the distribution of x, i.e. the variance that would never be eliminated even with perfect information about the underlying distribution model; 2) $\sigma_{n}^{2}$ - the variance introduced from the estimation of the mean vector "mu", this component can be eliminated given exact prior information or very large training set ( N goes to infinity);

The above figure illustrates the Bayesian inference for the mean of a Gaussian distribution, for which the variance is assumed to be known. The curves show the prior distribution over 'mu' (the curve labeled N=0), which in this case is itself Gaussian, along with the posterior distributions for increasing number N of data points. The figure makes clear that as the number of data points increase, the posterior distribution peaks around the true value of the mean. This phenomenon is known as *Bayesian learning*.