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ShanghaiTech University Knowledge Management System
| An adaptive approximate Bayesian computation MCMC with Global-Local proposals | |
| 2024-12-20 | |
| 状态 | 已发表 |
| 摘要 | In this paper, we address the challenge of Markov Chain Monte Carlo (MCMC) algorithms within the Approximate Bayesian Computation (ABC) framework, which often get trapped in local optima due to their inherent local exploration mechanism. We propose a novel Global- Local ABC-MCMC algorithm that combines the “exploration” capabilities of global proposals with the “exploitation” finesse of local proposals. By integrating iterative importance resampling into the likelihood-free framework, we establish an effective global proposal distribution. We select the optimum mixture of global and local moves based on a unit cost version of expected squared jumped distance via sequential optimization. Furthermore, we propose two adaptive schemes: The first involves a normalizing flow-based probabilistic distribution learning model to iteratively improve the proposal for importance sampling, and the second focuses on optimizing the efficiency of the local sampler by utilizing Langevin dynamics and common random numbers. We numerically demonstrate that our method improves sampling efficiency and achieve more reliable convergence for complex posteriors. A software package implementing this method is available at https://github.com/caofff/GL-ABC-MCMC. |
| 关键词 | Approximate Bayesian computation common random numbers iterative sampling importance resampling normalizing flow sequential optimization |
| 语种 | 英语 |
| DOI | arXiv:2412.15644 |
| 相关网址 | 查看原文 |
| 出处 | Arxiv |
| 收录类别 | PPRN.PPRN |
| WOS记录号 | PPRN:120115611 |
| WOS类目 | Statistics& Probability |
| 资助项目 | National Natural Science Foundation of China["12131001","12101333"] |
| 文献类型 | 预印本 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/483982 |
| 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, Tianjin, Peoples R China 2.ShanghaiTech Univ, Inst Math Sci, Shanghai, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cao, Xuefei,Wang, Shijia,Zhou, Yongdao. An adaptive approximate Bayesian computation MCMC with Global-Local proposals. 2024. |
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