时 间：2024年5月21日 15:00-16:00
地 点：普陀校区理科大楼A1514
报告人：上海科技大学 汪时嘉 研究员
主持人：华东师范大学 明静思助理研究员
摘 要：
Approximate Bayesian computation (ABC) is a class of Bayesian inference algorithms that targets for problems with intractable or unavailable likelihood function. It uses synthetic data drawn from the simulation model to approximate the posterior distribution. However, ABC is computationally intensive for complex models. Firstly, 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). Secondly, we introduce an adaptive ABC Markov chain Monte Carlo (MCMC) approach for multimodal distribution in high dimensional parameter space by combining the advantages of global and local MCMC proposals.
报告人简介：
报告人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。