文章基本信息

标题：Finding our Way in the Dark: Approximate MCMC for Approximate Bayesian Methods
本地全文：下载
作者：Evgeny Levi ; Radu V. Craiu
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2022
卷号：17
期号：1
页码：193-221
DOI：10.1214/20-BA1250
语种：English
出版社：International Society for Bayesian Analysis
摘要：With larger data at their disposal, scientists are emboldened to tackle complex questions that require sophisticated statistical models. It is not unusual for the latter to have likelihood functions that elude analytical formulations. Even under such adversity, when one can simulate from the sampling distribution, Bayesian analysis can be conducted using approximate methods such as Approximate Bayesian Computation (ABC) or Bayesian Synthetic Likelihood (BSL). A significant drawback of these methods is that the number of required simulations can be prohibitively large, thus severely limiting their scope. In this paper we design perturbed MCMC samplers that can be used within the ABC and BSL paradigms to significantly accelerate computation while maintaining control on computational efficiency. The proposed strategy relies on recycling samples from the chain’s past. The algorithmic design is supported by a theoretical analysis while practical performance is examined via a series of simulation examples and data analyses.
关键词：60J22;60K35;62-08;Approximate Bayesian Computation;k-Nearest Neighbour;Perturbed MCMC;synthetic likelihood