A generalization of Bayesian inference in the Dempster-Shafer belief theoretic framework

J. N. Heendeni and K. Premaratne and M. N. Murthi and J. Uscinski and M. Scheutz

In the literature, two main views of Dempster-Shafer (DS) theory are espoused: DS theory as evidence (as described in Shafer's seminal book) and DS theory as a generalization of probability. These two views are not always consistent. In this paper, we employ the generalized probability view of DS theory to arrive at results that allow one to perform Bayesian inference within the DS theoretic (DST) framework.

@inproceedings{heendenietal16fusion,
  title={A generalization of Bayesian inference in the Dempster-Shafer belief theoretic framework},
  author={J. N. Heendeni and  K. Premaratne and  M. N. Murthi and  J. Uscinski and  M. Scheutz},
  year={2016},
  booktitle={19th International Conference on Information Fusion},
  pages={798--804}
  url={https://hrilab.tufts.edu/publications/heendenietal16fusion.pdf}
}