# posterior probability

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Related to posterior probability: Prior probability

## posterior probability

n
(Statistics) statistics the probability assigned to some parameter or to an event on the basis of its observed frequency in a sample, and calculated from a prior probability by Bayes' theorem. Compare prior probability See also empirical5
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2]) is Posterior probability (or conditional probability) of [X.
The null model of no effects of any measured variables on p for any life stage of California Red-legged Frogs in coastal-dune drainages had the highest posterior probability, although there was considerable uncertainty about the effects of different variables on p (Table 2).
We should distinguish between sufficiency, which relates to the posterior probability after all the evidence is weighed (on atomic or holistic bases), and probativity, which relates to the support that each item of evidence (or a subset of the evidence) contributes.
According to the actual posterior probability of CI or ICH, an absolute binary answer of CI or ICH was made.
This linear discriminant model enables biologists to obtain the posterior probability that a harvested animal is from a given age class from its eye lens dry mass (Supplementary material Appendix 4 Table A2).
According to theorem 2, we can add the weight of the same variable nt to get the sample set [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] which is used to represent the posterior probability of the identity variable [pi]([n.
In this report we will display directly standardized plots of hospital posterior probability density functions for 30-day mortality under the HC model (without hospital characteristics) and then our expanded model.
Then, the posterior probability, which is unable to be computed can may be expressed as:
Totally 81 ROIs were shown with posterior probability larger than 0.
674); the second most supported model, Classical II--East, had a posterior probability of 0.
For completeness, we plot the posterior probability of expansion in Figure 3.
The goal is to find an optimal label x* that maximizes the posterior probability P(x | y) for a good segmentation of the image.

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