Multiple-site updates in maximum a posteriori and marginal posterior modes image estimation
作者:
Merrilee Hurn,
Christopher Jennison,
期刊:
Journal of Applied Statistics
(Taylor Available online 1993)
卷期:
Volume 20,
issue 5-6
页码: 155-186
ISSN:0266-4763
年代: 1993
DOI:10.1080/02664769300000063
出版商: Carfax Publishing Company
数据来源: Taylor
摘要:
We describe standard single-site Monte Carlo Markov chain methods, the Hastings and Metropolis algorithms, the Gibbs sampler and simulated annealing, for maximum a posteriori and marginal posterior modes image estimation. These methods can experience great difficulty in traversing the whole image space in a finite time when the target distribution is multi-modal. We present a survey of multiple-site update methods, including Swendsen and Wang's algorithm, coupled Markov chains and cascade algorithms designed to tackle the problem of moving between modes of the posterior image distribution. We compare the performance of some of these algorithms for sampling from degraded and non-degraded Ising models
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