• 李敬來教授學術報告

    發布時間:2020年11月06日 作者:王洪橋   消息來源:    閱讀次數:[]

    報告題目:Maximum Conditional Entropy Hamiltonian Monte Carlo Sampler

    報告人:李敬來教授,伯明翰大學

    報告時間:2020年11月6日16:00-17:00

    騰訊會議:931 916 378;密碼:1122

    報告摘要: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.

    報告人簡介:李敬來,英國伯明翰大學數學學院教授、博士生導師。博士畢業于紐約州立大學布法羅分校,曾在西北大學,MIT做博后研究工作。曾任上海交通大學特聘研究員,利物浦大學副教授。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等期刊發表SCI論文30余篇。主持國家自然科學基金2項。



    打印】【收藏】 【關閉

    时时彩注册