An instance crucial to most problems in signal processing is the selection of the order of a presupposed model. Examples are the determination of the putative number of signals present in white Gaussian noise or the number of noise-contaminated sources impinging on a passive sensor array. It is shown that maximum a posteriori Bayesian arguments, coupled with maximum entropy considerations, offer an operational and consistent model order selection scheme, competitive with the minimum description length criterion.
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