喻园管理论坛2023年第7期(总第830期)
演讲主题: A Scalable Gaussian Process for Large-Scale Periodic Data
主 讲 人: 李勇祥，上海交通大学工业工程与管理系副教授
主 持 人: 杨 超，管理学院管理科学与信息管理系教授
活动时间: 2023年2月20日（周一）上午10:00-12:00
活动地点: 管理大楼119教室
主讲人简介：
2023.01至今，上海交通大学机械与动力工程学院副教授
2019.09-2022.12，上海交通大学 机械与动力工程学院 助理教授
2013.08-2015.08，香港城市大学 系统工程与工程管理系 研究助理
2011.03-2013.06，光启高等理工研究院 超材料技术国家重点实验室 研究助理/工程师
李勇祥博士围绕大规模复杂系统的质量与可靠性等风险评估理论，研究大数据与工程机理联合驱动的复杂系统不确定性分析与量化。研究方向主要包括试验设计与分析、统计与机器学习，统计质量控制、统计信号处理等数据科学方法与大数据技术。
活动简介：
The periodic Gaussian process (PGP) has been increasingly used to model periodic data due to its high accuracy. Yet, computing the likelihood of PGP has a high computational complexity of O(n3) (n is the data size), which hinders its wide application. To address this issue, we propose a novel circulant PGP (CPGP) model for large-scale periodic data collected at grids that are commonly seen in signal processing applications. The proposed CPGP decomposes the log-likelihood of PGP into the sum of two computationally scalable composite log-likelihoods, which do not involve any approximations. Computing the likelihood of CPGP requires only O(p2) (or O(plogp) in some special cases) time for grid observations, where the segment length p is independent of and much smaller than n. Simulations and real case studies are presented to show the superiority of CPGP over some state-of-the-art methods, especially for applications requiring periodicity estimation. This new modeling technique can greatly advance the applicability of PGP in many areas and allow the modeling of many previously intractable problems.
