Bayesian probability

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Bayesian Probability

Bayesian probability (pronunciation: /beɪz.i.ən prɒb.əˈbɪl.ɪ.ti/) is a theory in the field of statistics that describes the mathematical representation of subjective belief. The theory is named after Thomas Bayes, an 18th-century British mathematician and Presbyterian minister.

Etymology

The term "Bayesian" refers to Thomas Bayes, who introduced the concept of conditional probability. Bayes's work was significantly developed by Pierre-Simon Laplace, who first published the modern formulation in his 1812 "Théorie analytique des probabilités."

Definition

In Bayesian probability, an individual's beliefs are described by a probability distribution. This belief may be updated using Bayes' theorem. The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation.

Related Terms

  • Bayes' theorem: A theorem describing how the conditional probability of each of a set of possible causes for a given observed outcome can be computed from knowledge of the probability of each cause and the conditional probability of the outcome of each cause.
  • Prior probability: The probability that an event will reflect established beliefs about this event before the arrival of new evidence.
  • Posterior probability: The revised probability of an event occurring after taking into consideration new information.
  • Likelihood function: A function that measures the goodness of fit of a statistical model to the data as a function of the parameters of the model.
  • Statistical inference: The process of using data analysis to deduce properties of an underlying probability distribution.

See Also

External links

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