Bayes' theorem

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Related to Bayesian updating: Bayesian analysis, Bayesian approach

Bayes' theorem

[¦bāz ′thir·əm]
(mathematics)
A theorem stating that the probability of a hypothesis, given the original data and some new data, is proportional to the probability of the hypothesis, given the original data only, and the probability of the new data, given the original data and the hypothesis. Also known as inverse probability principle.
References in periodicals archive ?
In Section IV, after first showing evidence of the validity of the estimated posteriors, I use ordinary least squares regressions to test the null hypothesis of Bayesian updating.
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.
This Bayesian updating is crucial in our two-period model, since in the second period, the buyer has no incentive to underreport.
To facilitate the Bayesian updating, this function will be replaced by a family of functions [F.
These values can be applied to the Bayesian updating.
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.
This article describes an experiment that introduces a simple insurance option into El-Gamal and Grether's (1995) test of Bayesian updating.
We use an experiment, described in the next section, in which others have found systematic deviations from Bayesian updating.
This suggests that deviations from the belief learning pattern can be attributed to imperfect Bayesian updating and not to sophisticated strategic reasoning.