Accuracy of approximation of Bayes estimates in presence of noninformative parameters
Abstract
The asymptotic behavior of multidimensional a posterior distributions as the signaltonoise ratio increases is used for construction of quasioptimum estimates. It is found that a maximumlikelihood estimate approximates a Bayes estimates with asymptotically decreasing mean square error. The procedure is applicable to a signal with a multidimensional Gaussian distribution of unknown parameters, calculation of the approximation error becoming quite simple when all unknown parameters are not energy related. A pulse signal with linear frequency modulation mixed with a white noise at the receiver input illustrates how the presence of a noninformative parameter, in this case the Gaussian random frequency, decreases the accuracy of this approximation for estimating the Gaussian random position of the signal in time.
 Publication:

USSR Rept Electron Elec Eng JPRS UEE
 Pub Date:
 January 1985
 Bibcode:
 1985RpEEE.......32T
 Keywords:

 Asymptotic Properties;
 Bayes Theorem;
 Estimates;
 Maximum Likelihood Estimates;
 Pulse Generators;
 Signal Analysis;
 Signal To Noise Ratios;
 Error Analysis;
 Mean Square Values;
 Normal Density Functions;
 Numerical Analysis;
 Random Noise;
 Communications and Radar