Bayes' theorem

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Related to Bayes's Theorem: conditional probability

Bayes' theorem

[¦bāz ′thir·əm]
(mathematics)
A theorem stating that the probability of a hypothesis, given the original data and some new data, is proportional to the probability of the hypothesis, given the original data only, and the probability of the new data, given the original data and the hypothesis. Also known as inverse probability principle.
References in periodicals archive ?
Bayes's theorem, a well-known theorem in probability, allows interpretation of findings on the basis of the actual data by providing a way to compute conditional probabilities of events.
If D represents some data obtained to evaluate the truth of proposition A, then we can write Bayes's theorem in its simplest form as
One focus of my current research, which involves, among other things, a new proof of Bayes's theorem, uses a calculus of variations information theoretic framework and generalisations of it, described in my works published since my 1988 American Statistician article [Zellner (1988)] with commentary by Edwin T.
Using a more general form of Bayes's Theorem, we then use the surname lists to update the prior probabilities of membership in each of the four race/ ethnic categories with the surname list results to produce efficient, updated posterior probabilities of membership in the four groups.
Holder analyzes the probabilities of these two options with the use of Bayes's theorem (described in an appendix) and concludes in his final chapter that "Theism Wins.
Bayes's theorem is: P(A|B) = P(B|A) x P(A) P(B) P stands for probability, A and B are events that could happen and | means given.
Other subjects are the role of endoplasmic reticulum-mediated apoptotic pathways in amyloid peptide toxicity, the P300 component of the event-related brain potential and Bayes's theorem, and the medial prefrontal cortex and Pavlovian conditioning.