Bayesian approach

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Bayesian Approach

The Bayesian approach (pronounced: bay-zee-an approach) is a statistical paradigm that deals with the interpretation of statistical procedures in terms of probability. The term is derived from the name of the mathematician Thomas Bayes, who provided the first mathematical formulation of this approach.

Etymology

The Bayesian approach is named after Thomas Bayes, an 18th-century English statistician and Presbyterian minister. Bayes is known for formulating a specific case of the theorem that bears his name, the Bayes' theorem, which is the foundation of the Bayesian approach.

Definition

In the Bayesian approach, all unknown quantities are treated as random variables. It 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. Bayesian statistics are characterized by mathematical concepts of probability, randomness and uncertainty.

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 an event before new evidence is taken into account.
  • 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.
  • Statistical inference: The process of using data analysis to deduce properties of an underlying probability distribution.

See Also

External links

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