Improved expected $L_2$-discrepancy formulas on jittered sampling
Jun Xian, Xiaoda Xu

TL;DR
This paper derives improved formulas for the expected $L_2$-discrepancy in jittered sampling, providing more accurate assessments of sampling uniformity.
Contribution
It introduces new explicit and exact formulas for expected $L_2$-discrepancy, surpassing previous jittered sampling estimates.
Findings
Derived explicit formulas for expected $L_2$-discrepancy.
Derived exact formulas for expected $L_2$-discrepancy.
Results show improved discrepancy estimates over traditional jittered sampling.
Abstract
We study the expected discrepancy under two classes of partitions, explicit and exact formulas are derived respectively. These results attain better expected discrepancy formulas than jittered sampling.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMathematical Approximation and Integration
