Stein's method of normal approximation: Some recollections and reflections
Not on the map. Why?
Abstract (arXiv)
This paper is a short exposition of Stein's method of normal approximation from my personal perspective. It focuses mainly on the characterization of the normal distribution and the construction of Stein identities. Through examples, it provides glimpses into the many approaches to constructing Stein identities and the diverse applications of Stein's method to mathematical problems. It also includes anecdotes of historical interest, including how Stein discovered his method and how I found an unpublished proof of his of the Berry-Esseen theorem.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Apr 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2026Minimax Lower Bounds of Kernel Discrepancy Estimation: MMD, HSIC, KSD27 Jul 2026
- 2026Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven7 May 2026
- 2025The Minimax Lower Bound of Kernel Stein Discrepancy Estimation16 Oct 2025
- 2024Nystr"om Kernel Stein Discrepancy12 Jun 2024