论文标题
预期$ l_2- $差异限制为一类新的分层采样模型
Expected $L_2-$discrepancy bound for a class of new stratified sampling models
论文作者
论文摘要
我们介绍了一类凸的e象分区。在这些分区中讨论了预期的$ l_2- $差异。有两个主要结果。首先，在这种分区下，我们生成的随机点集具有比相同采样数字的经典抖动抽样相比，预期$ l_2- $差异较小。其次，还给出了在这种分区下的明确预期$ L_2- $差异上限。此外，在这些新分区中，有最佳的预期$ L_2- $差异上限。
We introduce a class of convex equivolume partitions. 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. Second, an explicit expected $L_2-$discrepancy upper bound under this kind of partitions is also given. Further, among these new partitions, there is optimal expected $L_2-$discrepancy upper bound.
