Bayes' theorem


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Related to Bayes' theorem: conditional probability

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 ?
152) However, "objective Bayesians" (153) use Bayes' theorem without eliciting prior probabilities from subjective beliefs, avoiding the charge of subjectivism.
Let's analyze SPOT using Bayes' Theorem and some numerical approximations and conservative assumptions.
That being the case, Bayes' theorem tells us that when the level of statistical significance is set at P <0.
In both cases, the general Bayesian framework for continuous distributions uses the opinions of experts as "evidence," and this evidence is used as input to the decision maker's state of knowledge using Bayes' Theorem.
This bias may be corrected by application of Bayes' theorem [the Begg and Greenes' estimates (3,4)].
Bayes' Theorem posits that the posterior odds of the proposition equal the prior odds times the likelihood ratio.
Bayes' Theorem and Aristotle's Efficient-Final Cause Symmetry
Then Bayes' theorem was used to establish various decision rules.
They also draw on reprints from the statistical literature to reexamine sample selection, linear regression, the analysis of variance, maximum likelihood, Bayes' Theorem, meta-analysis, and the bootstrap.