# Difference between revisions of "Bayes' theorem"

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A law of probability that describes the proper way to incorporate new evidence into prior probabilities to form an updated probability estimate. Bayesian rationality takes its name from this theorem, as it is regarded as the foundation of consistent rational reasoning under uncertainty. A.k.a. "Bayes's Theorem" or "Bayes's Rule".

The theorem commonly takes the form:

${\displaystyle P(A|B)={\frac {P(B|A)\,P(A)}{P(B)}}}$

where A is the proposition of interest, B is the observed evidence, P(A) and P(B) are prior probabilities, and P(A|B) is the posterior probability of A.

With the posterior odds, the prior odds and the likelihood ratio written explicitly, the theorem reads:

${\displaystyle {\frac {P(A|B)}{P(\neg A|B)}}={\frac {P(A)}{P(\neg A)}}\cdot {\frac {P(B|A)}{P(B|\neg A)}}}$

or in words, ${\displaystyle Posterior~odds=Prior~odds\times Likelihood~ratio}$.