Focusing on a geometric perspective, this book presents an innovative approach to belief calculus and uncertainty theory. It conceptualizes uncertainty measures as points within a complex geometric space, allowing for manipulation such as combination and conditioning. This original view aims to make the concepts accessible to a broad audience, providing a fresh understanding of how uncertainty can be visualized and analyzed.
Some original perspectives on the intriguing mathematics and the practical use of belief functions
Evidential reasoning, rooted in the critique of classical Bayesian theory, offers a robust framework for modeling uncertainty and subjective beliefs. This book explores the intersection of computer vision and belief calculus, demonstrating how their integration can lead to significant advancements in both areas. It presents novel mathematical developments related to belief functions and proposes innovative solutions to fundamental challenges in computer vision, highlighting the potential for machines to gain human-like visual intelligence in complex environments.
Third International Conference, BELIEF 2014, Oxford, UK, September 26-28, 2014. Proceedings
This book constitutes the thoroughly refereed proceedings of the Third International Conference on Belief Functions, BELIEF 2014, held in Oxford, UK, in September 2014. The 47 revised full papers presented in this book were carefully selected and reviewed from 56 submissions. The papers are organized in topical sections on belief combination;