文章基本信息

标题：Constructing Metropolis-Hastings proposals using damped BFGS updates ⁎
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作者：Johan Dahlin ; Adrian Wills ; Brett Ninness 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：664-669
DOI：10.1016/j.ifacol.2018.09.208
语种：English
出版社：Elsevier
摘要：AbstractThe computation of Bayesian estimates of system parameters and functions of them on the basis of observed system performance data is a common problem within system identifcation. This is a previously studied issue where stochastic simulation approaches have been examined using the popular Metropolis-Hastings (MH) algorithm. This prior study has identified a recognised difficulty of tuning theproposal distributionso that the MH method provides realisations with sufficient mixing to deliver efficient convergence. This paper proposes and empirically examines a method of tuning the proposal using ideas borrowed from the numerical optimisation literature around efficient computation of Hessians so that gradient and curvature information of the target posterior can be incorporated in the proposal.
关键词：KeywordsBayesian parameter inferencestate-space modelsquasi-NewtonBFGS