Convergence assessment techniques for Markov chain Monte Carlo
Convergence assessment techniques for Markov chain Monte Carlo
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DOI：
10.1023/a:1008820505350
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发表时间：
1998-12-01
影响因子：
2.2
通讯作者：
Roberts, GO
中科院分区：
文献类型：
--
作者：
Brooks, SP;Roberts, GO
MCMC方法在过去几年中有效地革新了贝叶斯统计领域。这种方法提供了宝贵的工具，以克服固有的问题，采用贝叶斯方法的统计modeling.However，任何推理的基础上MCMC输出依赖于严格的假设，被模拟的马尔可夫链已达到稳定状态或“收敛”。许多技术已被开发，试图确定一个特定的马尔可夫链是否收敛，本文的目的是审查这些方法的数学基础上，这些技术的重点，试图总结目前的“国家的发挥”收敛评估技术，并激励在这一领域的未来研究方向。
MCMC methods have effectively revolutionised the field of Bayesian statistics over the past few years. Such methods provide invaluable tools to overcome problems with analytic intractability inherent in adopting the Bayesian approach to statistical modelling.However, any inference based upon MCMC output relies critically upon the assumption that the Markov chain being simulated has achieved a steady state or "converged". Many techniques have been developed for trying to determine whether or not a particular Markov chain has converged, and this paper aims to review these methods with an emphasis on the mathematics underpinning these techniques, in an attempt to summarise the current "state-of-play" for convergence assessment techniques and to motivate directions for future research in this area.