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Part of the explanation is likely to rest in using the maximum posterior network as the estimated network.
So too for the maximum posterior network — the single most probable network in a chain — unless its probability mass dominates a possibly multi-modal landscape, comprising a near-infinity of alternative models, its status as a representative estimator is questionable [50].
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From this initial network, greedy search algorithm with random restarts is performed to get the highest score posterior network to avoid local maxima.
Two important tasks in probabilistic reasoning are the computation of the maximum posterior probability of a given subset of the variables in a Bayesian network (MAP), and the computation of the maximum expected utility of a strategy in an influence diagram (MEU).
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.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com