The nature of the unnormalized beliefs encountered in the transferable belief model
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Artificial Intelligence
Representation, independence, and combination of evidence in the Dempster-Shafer theory
Advances in the Dempster-Shafer theory of evidence
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Joint tracking of manoeuvring targets and classification of their manoeuvrability
EURASIP Journal on Applied Signal Processing
Belief functions on real numbers
International Journal of Approximate Reasoning
Mass function derivation and combination in multivariate data spaces
Information Sciences: an International Journal
A static evidential network for context reasoning in home-based care
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Inference in possibilistic network classifiers under uncertain observations
Annals of Mathematics and Artificial Intelligence
Multisensor data fusion: A review of the state-of-the-art
Information Fusion
| Hi-index | 0.00 |
The Dezert-Smarandache theory (DSmT) and transferable belief model (TBM) both address concerns with the Bayesian methodology as applied to applications involving the fusion of uncertain, imprecise and conflicting information. In this paper, we revisit these concerns regarding the Bayesian methodology in the light of recent developments in the context of the DSmT and TBM. We show that, by exploiting recent advances in the Bayesian research arena, one can devise and analyse Bayesian models that have the same emergent properties as DSmT and TBM. Specifically, we define Bayesian models that articulate uncertainty over the value of probabilities (including multimodal distributions that result from conflicting information) and we use a minimum expected cost criterion to facilitate making decisions that involve hypotheses that are not mutually exclusive. We outline our motivation for using the Bayesian methodology and also show that the DSmT and TBM models are computationally expedient approaches to achieving the same endpoint. Our aim is to provide a conduit between these two communities such that an objective view can be shared by advocates of all the techniques.