On Marginally Correct Approximations of Dempster-Shafer Belief Functions
  from Data
  Mathematical Theory of Evidence (MTE), a foundation for reasoning under
partial ignorance, is blamed to leave frequencies outside (or aside of) its
framework. The seriousness of this accusation is obvious: no experiment may be
run to compare the performance of MTE-based models of real world processes
against real world data.
  In this paper we consider this problem from the point of view of conditioning
in the MTE. We describe the class of belief functions for which marginal
consistency with observed frequencies may be achieved and conditional belief
functions are proper belief functions,%\ and deal with implications for
(marginal) approximation of general belief functions by this class of belief
functions and for inference models in MTE.
