Asymptotic Behavior of the Bayes Estimates of a Signal Parameter in the Presence of Nonstationary Normal Noise

It is shown that for large classes of a priori distributions and loss functions the Bayes estimate reduces to the conditional maximum-likelihood estimate for an unbounded increase of the signal-to-noise ratio. An approximate calculation is carried out for the error of approximation of the Bayes estimate by its limiting value for large but finite signal-to-noise ratios.