Bayes' theorem

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(statistics) a theorem describing how the conditional probability of a set of possible causes for a given observed event can be computed from knowledge of the probability of each cause and the conditional probability of the outcome of each cause

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Proving history; Bayes's theorem and the quest for the historical Jesus.
Common sense, not to mention Bayes's Theorem, would suggest that the less likely a claim, the stronger the evidence must be to justify belief.
The article begins with a brief introduction to Bayes's theorem, followed by a discussion and comparison of classical and Bayesian approaches.
the use of Bayes's theorem, to calculate probabilities.
This conversation canvasses Zellner's transition from physics to economics, the reason for the renewal of interest in Bayes's theorem in the twentieth century, the empirical methodology of science underpinning the Chicago School and the influence of Alfred Marshall on Zellner's recent contributions to macroeconomic modelling.
In a research paper accepted for publication in Monthly Notices of the Royal Astronomical Society, they took data from WMAP and other cosmology experiments and analyzed it using Bayes's theorem, which can be used to show how the certainty attached to a particular conclusion is affected by different starting assumptions.
Bayes's theorem is commonly applied to medical diagnostic testing; in the context of evaluating diagnostic tests, the probability of a given individual having a disease depends both upon (1) an individual's prior probability of having the disease (usually determined from a base rate appropriate to the individual's risk group) and (2) the result of a diagnostic test.
The posterior is obtained by application of Bayes's theorem, which states that the posterior is proportional to the product of the prior and the likelihood of the sample, [Pi](Y|[Theta]), viz.
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.