bayesian estimation for the signal parameters in a gaussian random field

in recent years, some statisticians have studied the signal detection problem by using the random field theory. in this paper we have considered point estimation of the gaussian scale space random field parameters in the bayesian approach. since the posterior distribution for the parameters of interest dose not have a closed form, we introduce the markov chain monte carlo (mcmc) algorithm to approximate the bayesian estimations. we have also applied the proposed procedure to real fmri data, collected by the montreal neurological institute.