  Conditioning is crucial in applied science when inference involving time
series is involved. Belief calculus is an effective way of handling such
inference in the presence of epistemic uncertainty -- unfortunately, different
approaches to conditioning in the belief function framework have been proposed
in the past, leaving the matter somewhat unsettled. Inspired by the geometric
approach to uncertainty, in this paper we propose an approach to the
conditioning of belief functions based on geometrically projecting them onto
the simplex associated with the conditioning event in the space of all belief
functions. We show here that such a geometric approach to conditioning often
produces simple results with straightforward interpretations in terms of
degrees of belief. This raises the question of whether classical approaches,
such as for instance Dempster's conditioning, can also be reduced to some form
of distance minimisation in a suitable space. The study of families of
combination rules generated by (geometric) conditioning rules appears to be the
natural prosecution of the presented research.
