Bayesian inference

Bayesian inference applies the principles of Bayes' theorem and Bayesian probability to derive the rules of logic for propositions in which the degree of belief[?] isn't necessarily 1 (certainly true) or 0 (certainly false).

Bayesian inference has applications in artificial intelligence and expert systems.

For a worked example of one form of Bayesian inference, see naive Bayesian classification.

Bayesian inference and fuzzy logic are different approaches to the same tasks, but are not mathematically compatible.

External links:

Naive Bayesian learning paper (http://citeseer.nj.nec.com/30545.html)
A Tutorial on Learning With Bayesian Networks (http://citeseer.nj.nec.com/heckerman96tutorial.html)

		Bayesian inference
		Bayesian inference applies the principles of Bayes' theorem and Bayesian probability to derive the rules of logic for propositions in which the degree of belief[?] isn't necessarily 1 (certainly true) or 0 (certainly false). Bayesian inference has applications in artificial intelligence and expert systems. For a worked example of one form of Bayesian inference, see naive Bayesian classification. Bayesian inference and fuzzy logic are different approaches to the same tasks, but are not mathematically compatible. See also: Bayesian model comparison[?], Occam's Razor, Minimum description length[?] External links: Naive Bayesian learning paper (http://citeseer.nj.nec.com/30545.html) A Tutorial on Learning With Bayesian Networks (http://citeseer.nj.nec.com/heckerman96tutorial.html)
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