Belief Revision in Multimodal Sensing and Detection Environments

摘要

-like conditioning becomes a challenging task, primarily due to the complex nature of incoming evidence. In this article, a natural extension of Bayes-like conditioning applicable to belief revision in multimodal data fusion environments is explored. In particular, a detailed analysis of two functionally similar yet fundamentally different conditional approaches, namely,