Comparison of Bayesian and Dempster-Shafer Theory for Sensing: A Practitioner's Approach

This paper presents an applied practical comparison of Bayesian and Dempster-Shafer techniques useful for managing uncertainty in sensing. Three formulations of the same example are presented: a Bayesian, a naive Dempster-Shafer, and a Dempster-Shafer approach using a reened frame of discernment. Both the Bayesian and Dempster-Shafer (with a reened frame of discernment) yield similar results; however, information content and representations are diierent between the two methods. Bayesian theory requires a more explicit formulation of conditioning and the prior probabilities of events. Dempster-Shafer theory embeds conditioning information into its belief function and does not rely on prior knowledge, making it appropriate for situations where it is diicult to either collect or posit such probabilities, or isolate their contribution.