Randomized sequential importance sampling for estimating the number of perfect matchings in bipartite graphs

被引：2
|
作者：
Diaconis, Persi [1 ]
Kolesnik, Brett [2 ]
机构：
[1] Stanford Univ, Dept Math & Stat, Stanford, CA 94305 USA
[2] Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA
来源：
ADVANCES IN APPLIED MATHEMATICS | 2021年 / 131卷
基金：
加拿大自然科学与工程研究理事会;
关键词：
Asymptotic normality; Distributional recursion; Importance sampling; Monte Carlo methods; Perfect matchings; Randomized algorithms; RECURRENCE; PERMANENT;
D O I：
10.1016/j.aam.2021.102247
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
We introduce and study randomized sequential importance sampling algorithms for estimating the number of perfect matchings in bipartite graphs. In analyzing their performance, we establish various non-standard central limit theorems. We expect our methods to be useful for other applied problems. (c) 2021 Published by Elsevier Inc.
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页数：41
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