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Linea 1: {{in progressu}} [[Fasciculus:Bayes'_Theorem_MMB_01.jpg\|thumb\|~~A blue~~ [[Lumen nei~~\|neon sign~~]]~~, showing~~ ~~the~~caeruleum ~~simple~~formam ~~statement~~simplicem oftheorematis ~~Bayes’~~Bayes ~~theorem~~monstrans]] '''Theorema Bayes''' in [[theoria probabilitatum]] et [[statistica]] est [[theorema]] quod describit eventûs [[probabilitas\|probabilitatem]], fundatam in scientia priore condicionum quae cum hoc eventu coniunctae sint. Exempli gratia, si morbus [[cancer (morbus)]] cum aetate coniuncta sit, possumus ex aetate hominis probabilitatem illius eventûs, quod hic homo cancrum habeat, computare. <!-- In [[Theoria probabilitatum\|probability theory]] and [[Statistica\|statistics]], '''Bayes’ theorem''' (alternatively '''Bayes’ law''' or '''Bayes' rule''', also written as '''Bayes’s theorem''') describes the [[Probabilitas\|probability]] of an event, based on prior knowledge of conditions that might be related to the event. For example, if cancer is related to age, then, using Bayes’ theorem, a person’s age can be used to more accurately assess the probability that they have cancer, compared to the assessment of the probability of cancer made without knowledge of the person's age. One of the many applications of Bayes' theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in Bayes' theorem may have different probability interpretations. With the Bayesian probability interpretation the theorem expresses how a subjective degree of belief should rationally change to account for availability of related evidence. Bayesian inference is fundamental to Bayesian statistics.

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