文章基本信息

标题：Quasi-Newton particle Metropolis-Hastings *
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作者：Johan Dahlin ; Fredrik Lindsten ; Thomas B. Schön 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：28
页码：981-986
DOI：10.1016/j.ifacol.2015.12.258
语种：English
出版社：Elsevier
摘要：AbstractParticle Metropolis-Hastings enables Bayesian parameter inference in general nonlinear state space models (SSMs). However, in many implementations a random walk proposal is used and this can result in poor mixing if not tuned correctly using tedious pilot runs. Therefore, we consider a new proposal inspired by quasi-Newton algorithms that may achieve similar (or better) mixing with less tuning. An advantage compared to other Hessian based proposals, is that it only requires estimates of the gradient of the log-posterior. A possible application is parameter inference in the challenging class of SSMs with intractable likelihoods.We exemplify this application and the benefits of the new proposal by modelling log-returns offuture contracts on coffee by a stochastic volatility model with α-stable observations.
关键词：KeywordsBayesian parameter inferencestate space modelsapproximate Bayesian computationsparticle Markov chain Monte Carloα-stable distributions