Editing Bayes' theorem (section)

===Bayesian interpretation===
In the [[Bayesian probability|Bayesian (or epistemological) interpretation]], probability measures a "degree of belief".{{cn|date=April 2025}} Bayes' theorem links the degree of belief in a proposition before and after accounting for evidence. For example, suppose it is believed with 50% certainty that a coin is twice as likely to land heads than tails. If the coin is flipped a number of times and the outcomes observed, that degree of belief will probably rise or fall, but might remain the same, depending on the results. For proposition ''A'' and evidence ''B'',
* ''P'' (''A''), the ''prior'', is the initial degree of belief in ''A''.
* ''P'' (''A''&thinsp;|&thinsp;''B''), the ''posterior'', is the degree of belief after incorporating news that ''B'' is true.
* the quotient {{sfrac|''P''(''B''&thinsp;{{!}}&thinsp;''A'')|''P''(''B'')}} represents the support ''B'' provides for ''A''.

For more on the application of Bayes' theorem under the Bayesian interpretation of probability, see [[Bayesian inference]].