A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is
Advances in Intelligent and Soft Computing
We consider the case in which the available knowledge does not allow to specify a precise probabilistic model for the prior and/or likelihood in statistical estimation. We assume that this imprecision can be represented by belief functions. Thus, we exploit the mathematical structure of belief functions and their equivalent representation in terms of closed convex sets of probability measures to derive robust posterior inferences.doi:10.1007/978-3-642-29461-7_44 dblp:conf/belief/Benavoli12 fatcat:vrhjsz3a6nfpleht5eerpa6vpa