Bounding the convergence time of the Gibbs sampler in Bayesian image restoration
贝叶斯图像复原中Gibbs采样器收敛时间的界
This paper shows how coupling methodology can be used to give precise, a priori bounds on the convergence time of Markov chain Monte Carlo algorithms for which a partial order exists on the state space which is preserved by the Markov chain transitions. This methodology is applied to give a bound on the convergence time of the random scan Gibbs sampler used in the Bayesian restoration of an image of N pixels. For our algorithm, in which only one pixel is updated at each…