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报告题目:Deterministic Sampling of Expensive Posteriors Using Kullback-Leibler Divergence

报告人:孙法省 教授

报告摘要: This paper introduces a new way of discrete approximation a continuous probability distribution F into a set of representative points. These points are generated by minimizing the Kullback-Leibler divergence, a statistical potential measure of two probability distributions for testing goodness-of-fit. The Kullback- Leibler divergence is nonnegative, with the value is zero when the two probability distributions are equal. With this feature, we show that the empirical distribution of these representative points converges to the distribution F. The advantage of these points over Monte Carlo and other deterministic sampling are illustrated in the simulation. Two important applications of such points are then highlighted: (a) simulation from the complex probability densities, and (b) exploration and optimization of expensive black-box functions.


报告地点:腾讯会议ID号:241 776 834


报告人简介:孙法。笔Ψ洞笱Ы淌、博导,吉林省优秀教师。博士毕业于南开大学概率论与数理统计专业,分别在加拿大西蒙弗雷泽大学统计与保险系、加州大学洛杉矶分校统计系做访问学者。主要从事大数据抽样与分析、计算机试验设计与分析等方面的研究。获教育部高校科学研究优秀成果奖(科学技术)自然科学奖,全国统计科学研究优秀成果奖、吉林省自然科学学术成果奖。教育部长江学者奖励计划青年学者,已在统计学期刊 Annals of Statistics、 Journal of American Statistical Association、Biometrika 等期刊上发表论文20余篇,主持国家自然科学基金项目3项。