Monte Carlo and quasi-Monte Carlo methods for Dempster's rule of combination

被引：2
|
作者：
Salehy, Nima [1 ]
Okten, Giray [2 ]
机构：
[1] Louisiana Tech Univ, Dept Math & Stat, Ruston, LA 71272 USA
[2] Florida State Univ, Dept Math, Tallahassee, FL 32306 USA
来源：
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 2022年 / 145卷
关键词：
Dempster-Shafer theory; Theory of evidence; Data fusion; Monte Carlo; Quasi-Monte Carlo; Importance sampling; BELIEF FUNCTIONS;
D O I：
10.1016/j.ijar.2022.03.008
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
One of the challenges in Dempster-Shafer theory is that the data fusion calculation resulting from the popular Dempster's rule of combination is #P-complete. This imposes a computational constraint on the number of belief functions and the number of focal sets that can be combined using Dempster's rule. In this paper we develop Monte Carlo algorithms to approximate Dempster's rule of combination. The algorithms incorporate importance sampling and low-discrepancy sequences. Numerical results suggest the algorithms make it possible to apply Dempster's rule to a much larger number of belief functions and focal sets, and consequently widen the scope of applications of Dempster-Shafer theory. (c) 2022 Elsevier Inc. All rights reserved.
引用
收藏
页码：163 / 186
页数：24
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