MAXIMUM A-POSTERIORI MAXIMUM-ENTROPY ORDER DETERMINATION

An instance crucial. to most problems in signal processing is the sele ction of the order of a presupposed model. Examples are the determinat ion of the putative number of signals present in white Gaussian noise or the number of noise-contaminated sources impinging on a passive sen sor array. It is shown that maximum a posteriori Bayesian arguments, c oupled with maximum entropy considerations, offer an operational and c onsistent model order selection scheme, competitive with the minimum d escription length criterion.