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Revision as of 06:33, 13 September 2016
Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available.
Description
Bayesian inference is an important technique in statistics, and especially in mathematical statistics.
Bayesian updating is particularly important in the dynamic analysis of a sequence of data.
Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law.
In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability".
See also
- Bayes' theorem
- Bayesian Analysis, the journal of the ISBA
- Bayesian hierarchical modeling
- Bayesian probability
- Inductive probability
- International Society for Bayesian Analysis (ISBA)
- Jeffreys prior
- Bayesian Structural Time Series (BSTS)
- Monty Hall problem
External links
- Bayesian inference @ Wikipedia
