Linked from

The 36 pages that link to Bayes' theorem, each with the reason it gives.

• Bayesian inferenceRelated: It supplies the equation that turns prior probabilities and likelihoods into posterior probabilities.

• Probability theoryRelated: It updates a hypothesis’s probability using observed evidence.

• Prenatal diagnosisRelated: It explains why a positive screening result does not by itself establish that a fetus has a condition.

• ProbabilityRelated: It revises probabilities for hypotheses after evidence is observed.

• Conditional probabilityRelated: It computes a reverse conditional probability from a forward one and the relevant base rates.

• Likelihood functionRelated: In Bayesian inference, likelihood combines with a prior to produce a posterior distribution.

• Diagnostic testRelated: It combines test performance with the probability of disease before testing.

• Bayesian statisticsBroader topic: It gives the mathematical rule for turning prior probabilities and likelihoods into a posterior.

• Prior probabilityRelated: It combines a prior with a likelihood to calculate a posterior.

• Bayesian networksRelated: Inference uses this rule to revise probabilities when evidence is observed.

• Sensitivity and specificityRelated: It combines test performance with prior risk to estimate disease probability after a result.

• Bayesian probabilityRelated: It formalizes how Bayesian probability revises beliefs when evidence is observed.

• Event (probability theory)Related: It revises probabilities of hypotheses treated as events after evidence occurs.

• Bayesian decision theoryRelated: It supplies the probability updates that inform decisions under uncertainty.

• Likelihood ratioRelated: Bayes’ theorem turns prior odds and a likelihood ratio into posterior odds.

• Bayesian epistemologyRelated: It supplies the mathematical rule for revising belief after observing evidence.

• Radon–Nikodym theoremRelated: Density ratios supplied by the theorem express Bayesian updates in continuous models.

• Bayes factorNarrower topic: The odds form of this theorem gives the Bayes factor its updating role.

• Bayesian phylogeneticsNarrower topic: It is the probability-update rule underlying posterior probabilities for trees and model parameters.

• Beta distributionRelated: The beta distribution’s lasting statistical role grew from its conjugacy in Bayesian updating.

• Hidden Markov modelRelated: Inference uses observed data to revise probabilities over hidden states.

• Posterior probabilityRelated: It computes the posterior by combining the prior with the evidence’s likelihood.

• Base rate fallacyRelated: It gives the mathematical rule that prevents evidence from eclipsing the prior prevalence.

• Naive Bayes classifierRelated: It converts class likelihoods and prior probabilities into posterior class probabilities.

• Law of total probabilityCompared with: It uses total probability to reverse conditioning rather than merely decompose an event.

• Maximum a posteriori estimationRelated: It supplies the probability update whose posterior MAP estimation maximizes.

• Credible intervalRelated: It provides the update that produces the posterior distribution.

• Monty Hall problemRelated: It formalizes how the revealed door changes the odds for the unopened doors.

• Thomas BayesBroader topic: The theorem’s name preserves the connection to the inverse-probability work Bayes inspired.

• Statistical machine translationRelated: The noisy-channel formulation combines translation likelihood with target-language probability.

• Aumann's agreement theoremRelated: It supplies the familiar finite-probability form of posterior updating used in the theorem.

• Cox's theoremRelated: Once plausibilities obey probability calculus, Bayesian updating follows.

• History of statisticsBroader topic: Its publication in 1763 became a foundation for later approaches to statistical inference.

• Stigler's law of eponymyRelated: Thomas Bayes's result was developed and published after his death by Richard Price.

• Anne's theoremCompared with: Its distinctive standard title contrasts with names that cannot be tied to a specific result.

• Detection theoryRelated: It converts likelihood evidence and prior beliefs into posterior probabilities for decisions.