# Bayes' theorem

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Related to Bayes theorem: conditional probability

## Bayes' theorem

(beɪz)
n
(Statistics) statistics the fundamental result which expresses the conditional probability P(E/A) of an event E given an event A as P(A/E).P(E)/P(A); more generally, where En is one of a set of values Ei which partition the sample space, P(En/A) = P(A/En)P(En)/Σ P(A/Ei)P(Ei). This enables prior estimates of probability to be continually revised in the light of observations
[C20: named after Thomas Bayes (1702–61), English mathematician and Presbyterian minister]
ThesaurusAntonymsRelated WordsSynonymsLegend:
 Noun 1 Bayes' theorem - (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 causetheorem - an idea accepted as a demonstrable truthstatistics - a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters
References in periodicals archive ?
Bayes theorem provides a way of calculating posterior probability P(c|x) from P(c), P(x) and P(x|c).
Once again Sangero uses Bayes Theorem to illustrate his conclusion.
He was an amiable man, who liked to talk of what he felt was most important: the virtue of traditional Hindu women, why Sanskrit should be spoken in government offices, Gandhiji's mud pack cures, and the need to teach Bayes theorem to every luckless student in the land.
Consequently, the Naive Bayes theorem in the log domain is rewritten as follows:
From Bayes Theorem, p(winning | it has rained) = 0.
NaiveBayes is based on conditional probability, and following from Bayes theorem, for a document d and a class c, it is given as
They review areas such as Bayes Theorem, correlation and prediction, sampling models, psychometrics, inferring causality, Simpson's paradox, meta-analysis, and include a chapter on federal rules of evidence.
The book begins with a literature review of applications in engineering and an introduction to basic concepts of conditional probabilities and the Bayes Theorem.

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