Bayes' theorem

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Related to Bayesian updating: Bayesian analysis, Bayesian approach

Bayes' theorem

[bāz′]
Etymology: Thomas Bayes, British mathematician, 1702-1761
a mathematic statement of the relationships of test sensitivity, specificity, and the predictive value of a positive test result. The predictive value of the test is the number that is useful to the clinician. A positive result demonstrates the conditional probability of the presence of a disease.

theorem

(the'o-rem) [Gr. theorema, principle arrived at by speculation]
A proposition that can be proved by use of logic, or by argument, from information previously accepted as being valid.

Bayes' theorem

See: Bayes' theorem.
References in periodicals archive ?
These two features of the data--the single major signal for each team between observations of the voters' rankings, and knowledge of the signal distributions--distinguish the rankings data from most economic data, and allow the rankings to be used to study Bayesian updating.
Consequently, the estimated posteriors can really only be used to assess whether voters satisfy the necessary condition for Bayesian updating that future changes in ranks are not predictable using current information.
Employers are assumed to use Bayesian updating when forming beliefs about the ability of workers.
Using Bayesian updating in the specification of employer beliefs has the advantage of implying that, if there are systematic differences in ability across worker types, employers gradually learn to show preference for the higher ability types and, thus, to be willing to pay them higher wages.
The hypothesis, that [Delta] is equal to zero and that no Bayesian updating occurs, is not supported by the test results.
The DB-DC WTP estimate is not as conservative a welfare measure if Bayesian updating is incorporated into the model.
We use an experiment, described in the next section, in which others have found systematic deviations from Bayesian updating.
We consider rounds in which use of the representative or conservative heuristic leads to a different choice than Bayesian updating would predict.
In the game against nature, a notable difference between the actual updating process and the ideal Bayesian updating process is that people tend to underweight the impact of new evidence.
Note that the Bayesian updating process without strategic considerations is not naive in the game against nature.
The only point evident to me is to suggest that severe testing of our conjectures, and not anything resembling induction or Bayesian updating, is all that science requires.

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