Pattern Recognition

4Parameter estimation

The previous Chapter assumed that the quantities

$P\left(\omega |m\right)=\frac{p\left(m|\omega \right)P\left(\omega \right)}{p\left(m\right)}\alpha p\left(m|\omega \right)P\left(\omega \right)\left(4.1\right)$

(see Equation (3.32)) are known or at least the very right side (without p(m)). As already stated, the methods for determining these quantities can basically be divided into two groups, parametric and non-parametric methods. This chapter deals with the parametric approach.

In principle, one already has a mathematical model p(m|ωi, θ) of the distribution that bounds the major traits and restricts the degrees of freedom to a finite-dimensional parameter vector θ ∈ q. For example, ...