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Regions are classified into the mixture components (clusters) using the maximum posterior allocation rule.
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The test point is classified by the maximum posterior probability.
After all, assuming unimodality of the posterior function, one can get the optimal maximum posterior solution for the maximum likelihood estimate.
This article discusses a new approach to map accuracy assessment based on maximum posterior probability estimators.
The sample belongs to the class with maximum posterior probability for the sample.
We also provide the Expectation Maximization parameters' estimation and the maximum posterior marginal's restoration procedures.
Maximum posterior probability (MAP) criterion is adopted to perform spectrum sensing.
Both the models demonstrated the highest stresses at the mid-palatal suture, with maximum posterior dislocation.
In addition to discussing maximum posterior probability estimators, this article reports on a simulation study comparing three approaches to estimating map accuracy: 1) post-classification sampling, 2) resampling the training sample via cross-validation, and 3) maximum posterior probability estimation.
In cases of significant differences we calculated Bayesian maximum posterior estimates as well as highest posterior density intervals with 95% support (HPDI95%) for the interaction effects.
Part of the explanation is likely to rest in using the maximum posterior network as the estimated network.
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