Prior probability

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Prior Probability

Prior probability, pronounced as /praɪər prɒbəˈbɪlɪti/, is a fundamental concept in Bayesian statistics. The term originates from the Latin prior, meaning "before".

Definition

In Bayesian statistics, the prior probability of a random event or an uncertain proposition is the best rational assessment of the probability of the event or proposition before (prior to) any empirical data or evidence is taken into account.

Usage

Prior probability is used in Bayesian inference, a method of statistical inference where Bayes' theorem is used to update the probability of a hypothesis as more evidence or information becomes available. Prior probability is contrasted with posterior probability, the revised probability of an event occurring after taking into consideration new information.

Related Terms

  • Bayesian statistics: A theory in the field of statistics based on the Bayesian interpretation of probability where probability expresses a degree of belief in an event.
  • Posterior probability: The revised probability of an event occurring after taking into consideration new information.
  • Probability theory: A branch of mathematics concerned with probability, the analysis of random phenomena.
  • 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.

See Also

External links

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