  This paper will focus on the process of 'fusing' several observations or
models of uncertainty into a single resultant model. Many existing approaches
to fusion use subjective quantities such as 'strengths of belief' and process
these quantities with heuristic algorithms. This paper argues in favor of
quantities that can be objectively measured, as opposed to the subjective
'strength of belief' values. This paper will focus on probability
distributions, and more importantly, structures that denote sets of probability
distributions known as 'credal sets'. The novel aspect of this paper will be a
taxonomy of models of fusion that use specific types of credal sets, namely
probability interval distributions and Dempster-Shafer models. An objective
requirement for information fusion algorithms is provided, and is satisfied by
all models of fusion presented in this paper. Dempster's rule of combination is
shown to not satisfy this requirement. This paper will also assess the
computational challenges involved for the proposed fusion approaches.
