In statistics, Bayesian inference is a method of inference in which Bayes' rule is used to update the probability estimate for a hypothesis as additional evidence is learned. Bayesian updating is an important technique throughout statistics, and especially in mathematical statistics: Exhibiting a Bayesian derivation for a statistical method automatically ensures that the method works as well as any competing method, for some cases. Bayesian updating is especially important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a range of fields including science, engineering, medicine, and law.
In the philosophy of decision theory, Bayesian inference is closely related to discussions of subjective probability, often called "Bayesian probability." Bayesian probability provides a rational method for updating beliefs; however, non-Bayesian updating rules are compatible with rationality, according to philosophers Ian Hacking and Bas van Fraassen.
Read more about Bayesian Inference: Inference Over Exclusive and Exhaustive Possibilities, In Frequentist Statistics and Decision Theory, Bayes and Bayesian Inference, History
Famous quotes containing the word inference:
“The inference is, that God has restated the superiority of the West. God always does like that when a thousand white people surround one dark one. Dark people are always “bad” when they do not admit the Divine Plan like that. A certain Javanese man who sticks up for Indonesian Independence is very lowdown by the papers, and suspected of being a Japanese puppet.”
—Zora Neale Hurston (1891–1960)