Optimal expected L2−discrepancy bound for new stratified random sampling

发布时间：2022-06-21 作者：浏览次数：

Speaker:	冼军	DateTime:	2022-6-28(星期二)上午9:00-10:00
Brief Introduction to Speaker: 冼军，中山大学数学学院教授，博士生导师，主要从事应用调和分析、小波分析理论、采样理论及其应用等方面的研究工作，2014年获得国家自然科学基金优秀青年基金，2010年入选广东省“千百十”人材工程培养计划。曾应邀出访美国耶鲁大学、德国亚琛工业大学、新加坡国立大学、加拿大阿尔伯塔大学等。		Place:	六号楼二楼报告厅 M201
Abstract:We introduce a class of convex equivolume partitions, among this class, there exists an optimal partition manner. Expected $L_2-$discrepancy are discussed under these partitions. There are two main results. First, under this kind of partitions, we generate random point sets with smaller expected $L_2-$discrepancy than classical jittered sampling for the same sampling number. Further, among these new partitions, there is the optimal partition for the expected $L_2-$discrepancy. Second, an explicit expected $L_2-$discrepancy upper bound under this kind of partitions is also given.