Asymptotic properties of Bayes estimates of parameters of a signal masked by interference

It is shown that for a class of cost functions and a class of a priori distributions on the parameters to be estimated, the risks associated with the Bayes and maximum-likelihood estimates of the signal parameters become equal as the number of observations becomes large. This implies that the optimality properties usually attributed to Bayes estimates can be attributed in some cases to maximum-likelihood estimates as well. The work is an extension of known results to the case in which the statistical description of the observed process may be nonstationary.