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The decision is made using the maximum likelihood rule.
Firstly, the recursive equations are inferred based on the maximum likelihood rule.
Conditional probabilities are then multiplied and the maximum likelihood rule is applied.
A thematic map of benthic habitats was produced utilizing a supervised classification with the maximum likelihood rule on a natural color mosaic of high resolution satellite scenes (overall accuracy 80%, Kappa = 0.69).
If we treat w d as noise, then the maximum likelihood rule for deciding between two possible codewords for MPSK modulated systems, c α and c β, is |{mathbf y}_{d}-sqrt{{E_{c}}} {mathbf X}_{alpha} hat{textbf{h}}_{d}|^{2} overset{{mathbf c}_{alpha}}{underset{{mathbf c}_{beta}}{lessgtr}} |{mathbf y}_{d}-sqrt{{E_{c}}} {mathbf X}_{beta} hat{textbf{h}}_{d}|^{2}, (35).
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The maximum likelihood decision rule is implemented by using the acceptance set (18).
In particular, the acceptance set can be assigned according to the maximum likelihood decision rule.
The probabilities introduced above can be easily estimated for, which corresponds to the maximum likelihood decision rule.
To decode, a scalar correlation is computed and the final decision is computed with a maximum likelihood decision rule.
To classify a document, we employ a modified maximum likelihood decision rule, constructed so as to bias the decision towards the safe "mix" classification.
Based on the maximum likelihood decision rule, the transmitted signal is detected as b ^ = arg max j = 0, 1 r j (8).
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maximum amplitude rule
maximum likelihood estimation/estimator
maximum likelihood reconstruction
maximum likelihood ratio
maximum stay rule
maximum likelihood dendrogram
maximum likelihood learning
maximum likelihood tree
maximum likelihood estimation
maximum likelihood approach
maximum membership rule
maximum likelihood estimator
maximum probability rule
maximum likelihood Estimation
maximum deviation rule
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