华东交通大学学报2018，Vol.35Issue(1)：1-8,8.
Bayes统计学与MCMC方法——Metropolis-Hastings(M-H)算法的Matlab程序实现
Bayesian Statistics and MCMC Method——Matlab Programming for Metropolis-Hastings(M-H)Algorithm
摘要
Abstract
Bayes statistics in a vacuum can be broadly applied to the fields of natural science,economics and so-cial science, which benefits from the development of computer science and technology and Markov chain Monte Carlo (MCMC)method.In this paper the application of MCMC method into Bayes inference was introduced,and independent sampling and random walk sampling of Metropolis-Hastings(M-H) algorithm were mainly discussed. The two sampling modes were readably programmed with Matlab, and their detailed implementation processes, merits and demerits were talked about. It was shown by present simulation that, the independent sampling mode is relatively easy to implement, but need the proposal distribution to be close to the posterior distribution; other-wise, the calculation efficiency is low and the simulation effect is unsatisfactory. The random walk sampling mode don't need the proposal distribution to approach the posterior distribution and its simulation results are satisfactory. Therefore, it overcomes the limitations of the independent sampling mode has more widespread application.关键词
Bayes/MCMC/M-H法/Matlab程序Key words
Bayes/MCMC method/M-H algorithm/Matlab program分类
社会科学引用本文复制引用
陈梦成,方苇,杨超,谢力..Bayes统计学与MCMC方法——Metropolis-Hastings(M-H)算法的Matlab程序实现[J].华东交通大学学报,2018,35(1):1-8,8.基金项目
国家自然科学基金(51378206) （51378206）
