posterior probability

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Related to posterior probability: Prior probability

pos·te·ri·or prob·a·bil·i·ty

the best rational assessment of the probability of an outcome on the basis of established knowledge modified and brought up to date.
See also: prior probability, Bayes theorem.
References in periodicals archive ?
45 Table2 Conditional probabilities of variables M, E, H, express the knowledge that the presence of different defaults in the motor-pump M = T M = F E = T E = F E = T E = F H = T H = F H =T H = F H = T H = F H =T H = F S = T 1 1 1 1 1 1 1 1 S = F 0 0 0 0 0 0 0 0 Table 3 Posterior probability of the top event for the first scenario with uncertainty on [beta] and [eta] [eta] [beta] [[eta].
3 (a) lists in the tracking process identification in some tracking results, 3 (b) draws the in the test process, after the first five largest recognition probability with time variation diagrams, which, after the red solid line represents the real object recognition probability and the rest of the dotted line said other four largest recognition of posterior probability.
Finally, we believe that in one form or another, the full posterior probability density function needs to be conveyed to the public.
By assuming that normal distribution for each ROI inside temporal block and ROIs are independent of each other, the BMCPM calculates the posterior probability of the temporal block indicator vectors using the conjugate prior of N-Inv-[chi square].
That is maximizing the posterior probability is also equal to the minimization of the posterior energy function.
The group and catastrophic mortality scenarios, with or without predation mortality added, did not provide a good fit to the data by themselves, and received little posterior probability.
The posterior probability of a non-null impact was large in the Milan and Brescia areas and in other urban centers of the Po valley (Figure 2D).
The naive Bayesian classifier will predict that an instance X belongs to the class having the highest posterior probability, conditioned on X.
Bayesian theorem aims to use known information to construct the posterior probability density of system status variances, which means utilizing the model to predict the prior estimated density of the status, and then using the latest observation information to rectify and thus get probability density.
Within the Bayesian context we can obtain the posterior distribution for each Pearson residual and calculate the corresponding 95% posterior probability intervals.

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