Maximum Likelihood Estimation for Spatial Models by Markov Chain Monte Carlo Stochastic Approximation
Maximum Likelihood Estimation for Spatial Models by Markov Chain Monte Carlo Stochastic Approximation
Summary We propose a two-stage algorithm for computing maximum likelihood estimates for a class of spatial models. The algorithm combines Markov chain Monte Carlo methods such as the Metropolis–Hastings–Green algorithm and the Gibbs sampler, and stochastic approximation methods such as the off-line average and adaptive search direction. A new criterion …