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Abstract

In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule) describes the 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 than can be done without knowledge of the person’s age. Bayesian inference is fundamental to Bayesian statistics. Bayes' theorem is named after Reverend Thomas Bayes (1701–1761), who first used conditional probability to provide an algorithm that uses evidence to calculate limits on an unknown parameter. His work was published as An Essay Towards Solving a Problem in the Doctrine of Chances (1763).

Keywords

Alternative Bayes' Rule Bayes' Theorem Bayesian Statistics Conditional Probability Event Probability

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Payendah, A. (2020). Bayes’ Theorem, Amazement, and Its Application. Journal of Natural Sciences – Kabul University, 3(1), 209–218. https://doi.org/10.62810/jns.v3i1.148
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