Bayes' theorem

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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.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
In addition, we designed an alternative strategy: the development of a Bayesian network to examine the relationship between the selected variables in probabilistic terms (via Bayes's theorem).
In Research on Social Work Practice, Wolf-Branigin and Duke (2007) attempted to use Bayes's theorem to compute probabilities of involvement in spiritual activities and completing a Salvation Army substance abuse treatment program.
The notation for conditional probability is needed to illustrate Bayes's theorem. If A and I are propositions, we write a conditional probability as P(A 11), which is the probability that A is true, given that I is true.
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.
the hypotheses being that the population size is 10 and 1 million, respectively) and where the probability of the evidence (that "you" receive body number 7) is P ([e\h.sub.10]) = 0.1 and P(e\[h.sub.1M]) = 0.000001, then by Bayes's theorem:
For in view of Bayes's Theorem, the conditional probability of such a belief upon any evidence that is possible given one's initial beliefs, will always be equal to one.
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." That title is a bit too triumphal for my taste, but it does not affect the strength of the argument.