报告题目：Maximum Conditional Entropy Hamiltonian Monte Carlo Sampler

报告人：李敬来教授，伯明翰大学

报告时间：2020年11月6日16:00-17:00

腾讯会议：931 916 378；密码：1122

报告摘要：The performance of Hamiltonian Monte Carlo (HMC) sampler depends critically on some algorithm parameters such as the total integration time and the numerical integration stepsize. The parameter tuning is particularly challenging when the mass matrix of the HMC sampler is adapted. We propose in this work a Kolmogorov-Sinai entropy (KSE) based design criterion to optimize these algorithm parameters, which can avoid some potential issues in the often used jumping-distance based measures. For near-Gaussian distributions, we are able to derive the optimal algorithm parameters with respect to the KSE criterion analytically. As a byproduct the KSE criterion also provides a theoretical justification for the need to adapt the mass matrix in HMC sampler. Based on the results, we propose an adaptive HMC algorithm, and we then demonstrate the performance of the proposed algorithm with numerical examples.

报告人简介：李敬来，英国伯明翰大学数学学院教授、博士生导师。博士毕业于纽约州立大学布法罗分校，曾在西北大学，MIT做博后研究工作。曾任上海交通大学特聘研究员，利物浦大学副教授。在Journal of Computational Physics. SIAM Journal on Scientific Computing. Physical Review Letters. Inverse Problems. Journal of Computational Physics. SIAM Journal on Imaging Sciences. SIAM/ASA Journal on Uncertainty Quantification 和 Journal of Computational Physics等期刊发表SCI论文30余篇。主持国家自然科学基金2项。