Markov Chain Monte Carlo in small worlds

被引:0
作者
Yongtao Guan
Roland Fleißner
Paul Joyce
Stephen M. Krone
机构
[1] University of Idaho,Department of Mathematics
[2] University of Idaho,Department of Statistics
来源
Statistics and Computing | 2006年 / 16卷
关键词
Markov Chain Monte Carlo; Metropolis-Hastings algorithm; Proposal distributions; Small-world networks; Importance sampling;
D O I
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中图分类号
学科分类号
摘要
As the number of applications for Markov Chain Monte Carlo (MCMC) grows, the power of these methods as well as their shortcomings become more apparent. While MCMC yields an almost automatic way to sample a space according to some distribution, its implementations often fall short of this task as they may lead to chains which converge too slowly or get trapped within one mode of a multi-modal space. Moreover, it may be difficult to determine if a chain is only sampling a certain area of the space or if it has indeed reached stationarity.
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页码:193 / 202
页数:9
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