时 间：2025年5月26日9（周一）14：00 – 15：00
地 点：理科大楼A1714室
报告人：汪时嘉上海科技大学数学科学研究所助理教授
主持人：王亚平 华东师范大学教授
摘 要：
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. We integrate iterative importance resampling into the likelihood-free framework to establish an effective global proposal distribution, and select the optimum mixture of global and local moves based on a relative version of expected squared jumped distance via sequential optimization. Furthermore, we propose two adaptive schemes. The first adaptive scheme is a normalizing flow-based probabilistic distribution learning model to iteratively improve the proposal of importance sampling. The second adaptive scheme is optimizing the efficiency of the local sampler by utilizing Langevin dynamics and common random numbers. We numerically demonstrate that our method is able to improve sampling efficiency and achieve more reliable convergence for complex posteriors.
报告人简介：
汪时嘉博士是上海科技大学数学科学研究所的助理教授、研究员、博士生导师。 2019年在加拿大西蒙佛雷泽大学获得统计学博士学位，2019-2023年在南开大学任教。研究兴趣主要包括贝叶斯统计、统计机器学习以及进化生物学等。