Using Early Rejection Markov Chain Monte Carlo and Gaussian Processes to Accelerate ABC Methods

doi:10.1080/10618600.2024.2379349

KMS > 数学科学研究所

	Using Early Rejection Markov Chain Monte Carlo and Gaussian Processes to Accelerate ABC Methods
	Cao, Xuefei 1; Wang, Shijia2 ; Zhou, Yongdao 1
	2024-09-01
发表期刊	JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS (IF:1.4[JCR-2023],2.1[5-Year])
ISSN	1061-8600
EISSN	1537-2715
DOI	10.1080/10618600.2024.2379349
摘要	Approximate Bayesian computation (ABC) is a class of Bayesian inference algorithms that targets problems with intractable or unavailable likelihood functions. It uses synthetic data drawn from the simulation model to approximate the posterior distribution. However, ABC is computationally intensive for complex models in which simulating synthetic data is very expensive. In this article, we propose an early rejection Markov chain Monte Carlo (ejMCMC) sampler based on Gaussian processes to accelerate inference speed. We early reject samples in the first stage of the kernel using a discrepancy model, in which the discrepancy between the simulated and observed data is modeled by Gaussian process (GP). Hence, synthetic data is generated only if the parameter space is worth exploring. We demonstrate through theory, simulation experiments, and real data analysis that the new algorithm significantly improves inference efficiency compared to existing early-rejection MCMC algorithms. In addition, we employ our proposed method within an ABC sequential Monte Carlo (SMC) sampler. In our numerical experiments, we use examples of ordinary differential equations, stochastic differential equations, and delay differential equations to demonstrate the effectiveness of the proposed algorithm. We develop an R package that is available at https://github.com/caofff/ejMCMC.
关键词	Approximate Bayesian computation Early rejection Gaussian process Markov chain Monte Carlo Sequence Monte Carlo
URL	查看原文
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China["12131001","12101333"]
WOS研究方向	Mathematics
WOS类目	Statistics & Probability
WOS记录号	WOS:001316588700001
出版者	TAYLOR & FRANCIS INC
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/372914
专题	数学科学研究所数学科学研究所_PI研究组(P)_汪时嘉组
通讯作者	Wang, Shijia; Zhou, Yongdao
作者单位	1.Nankai Univ, Sch Stat & Data Sci, NITFID, 94 Weijin Rd, Tianjin 300071, Peoples R China 2.ShanghaiTech Univ, Inst Math Sci, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China
通讯作者单位	数学科学研究所
推荐引用方式 GB/T 7714	Cao, Xuefei,Wang, Shijia,Zhou, Yongdao. Using Early Rejection Markov Chain Monte Carlo and Gaussian Processes to Accelerate ABC Methods[J]. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS,2024.
APA	Cao, Xuefei,Wang, Shijia,&Zhou, Yongdao.(2024).Using Early Rejection Markov Chain Monte Carlo and Gaussian Processes to Accelerate ABC Methods.JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS.
MLA	Cao, Xuefei,et al."Using Early Rejection Markov Chain Monte Carlo and Gaussian Processes to Accelerate ABC Methods".JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS (2024).