Parametric estimation of Dempster-Shafer belief functions

Dempster-Shafer theory of evidence offers a natural setting for representing imprecise and uncertain information stemming from several sources. The application of the evidence theory in fusing information coming from different sources still poses certain problems. Of paramount importance is the problem of estimating the belief functions. Due to the coherence of this theory with the Bayesian approach, the belief functions can be represented by probabilities (a priori and a posteriori probabilities). In this paper, we propose an algorithm to estimate these belief functions. The algorithm is iterative and based on the maximum likelihood estimators. The interest of the proposed algorithm and its potential are studied starting from a few simple simulations.