AIMar 27, 2013
On the Combinality of Evidence in the Dempster-Shafer Theory
arXiv:1304.3119v1
Originality Synthesis-oriented
AI Analysis
This clarifies a foundational aspect of uncertainty reasoning for AI and decision-making systems, but it appears incremental as it focuses on an existing assumption.
The paper addresses the validity of the Dempster-Shafer rule of combination in evidence theory, finding that statistical independence of sources is the key restriction, allowing application to distinct probability distributions.
In the current versions of the Dempster-Shafer theory, the only essential restriction on the validity of the rule of combination is that the sources of evidence must be statistically independent. Under this assumption, it is permissible to apply the Dempster-Shafer rule to two or mere distinct probability distributions.