The information-fusion method of Bayes' decision theory is Bayes' rule. This method assigns an object to the class that fulfills a given criterion of similarity in the best way.

Bayes' decision theory is a fundamental statistical approach to the problem of pattern classification. This approach is based on the assumption that the decision problem is posed in probabilistic terms, and that all of the relevant probability values are known.

Bayes' rule is described in the following articles: [Celeux et al., 1989], [Milgram, 1993], or [Ma, 1995], and in the book [Duda and Hart, 1973].

[Haralick and Shapiro, 1993a] and [Sonka et al., 1993] present the use of this rule in pattern recognition framework.

• Review of Probability Theory
• Event
• Probability
• Random Variable and Probability Distribution
• Expectation
• Classifiers
• An Example
• Bayes' Decision Theory
• Example
• Classifier
• Parameters
• Conclusion