ABSTRACT
Bayesian theory has had a long and profound influence on statistical modeling. Two key elements make up a Bayesian classification formula, namely, the prior and conditional probability density functions (p.d.f.). By combining these functions, a classification can be expressed in terms of maximum a posteriori (MAP) criteria (Chapter 2). In practice, there are difficulties in using MAP estimates. One of the problems is that prior information or information concerning the data distribution may not always available. As a result, alternative criteria must be used in place of MAP. For example, if knowledge of data distributions is available, but not prior information about the data under consideration, then the maximum likelihood (ML) criterion may be used. Conversely, if one has prior information but no knowledge about the data distribution, then the maximum entropy criterion can be employed (Jaynes, 1982).
