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The test point is classified by the maximum posterior probability.
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.
Maximum posterior probability (MAP) criterion is adopted to perform spectrum sensing.
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.
Specifically, we formulate human localization problem as finding a location with the maximum posterior probability given the observed received signal strength indicator from passive radio-frequency identification tags.
The maximum posterior probability approach may also be used to increase the precision of estimates obtained from a post-classification sample.
Then, the two echo values associated to each voxel are checked, and the voxel is assigned the tissue class with maximum posterior probability.
More formally, we are given a sequence of observations and are required to determine a sequence of labels, that is, the sequence,, with maximum posterior probability.
By achieving the optimal solution corresponding to maximum posterior probability distribution, the low-frequency background of subsurface parameters can be obtained successfully.
Maximum posterior probability estimators are resistant to bias induced by non-representative sampling, and so are intended for situations in which the training sample is collected without using a statistical sampling design.
Related(20)
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