The Cumulative Rule for Belief Fusion
Audun Josang
Abstract
The problem of combining beliefs in the Dempster-Shafer belief theory has attracted considerable attention over the last two decades. The classical Dempster's Rule has often been criticised, and many alternative rules for belief combination have been proposed in the literature. The consensus operator for combining beliefs has nice properties and produces more intuitive results than Dempster's rule, but has the limitation that it can only be applied to belief distribution functions on binary state spaces. In this paper we present a generalisation of the consensus operator that can be applied to Dirichlet belief functions on state spaces of arbitrary size. This rule, called the cumulative rule of belief combination, can be derived from classical statistical theory, and corresponds well with human intuition.
Create a lesson
Related papers
WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill Evolution
Liyan Tang, Cyrus Rashtchian, Chun-Sung Ferng et al.
Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation
Nguyen Xuan-Vu, Octavian Susanu, Daniel Armstrong et al.
Learning a Continuous Sepsis Severity Score Without Hour-by-Hour Supervision: A Two-Site Retrospective Study
Kevin Zhu, Ryan Zhang, Baraa Abed et al.
CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Bases
Sil Hamilton, Albert Yu Sun, Oscar J. Romero et al.
Sophistication in GenAI Use: Field Evidence from a Large Firm
Nicholas J. Hallman, Zachary T. Kowaleski, Anu Puvvada et al.
Not All Eval-Awareness Is Equal: Capabilities Framing Predicts Compliance
Allison Zhuang, Santiago Aranguri