Bayes' theorem
Sign in to savetheorem describing the probability of an event based on prior knowledge of conditions that might be related to the event
AI overview
Bayes' theorem is a mathematical rule that helps you figure out how likely something is to happen by taking into account what you already know about related conditions. It matters because it provides a logical way to update your beliefs as you get new information, making it useful for everything from medical diagnosis to decision-making.
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Article
Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes (/beɪz/), gives a mathematical rule for inverting conditional probabilities, allowing the probability of a cause to be found given its effect. For example, with Bayes' theorem, the probability that a patient has a disease given that they tested positive for that disease can be found using the probability that the test yields a positive result when the disease is present. The theorem was developed in the 18th century by Bayes and independently by Pierre-Simon Laplace.
One of Bayes' theorem's many applications is Bayesian inference, an approach to statistical inference, where it is used to invert the probability of observations given a model configuration (i.e., the likelihood function) to obtain the probability of the model configuration given the observations (i.e., the posterior probability).