Non-Destructive Sample Generation From Conditional Belief Functions
  This paper presents a new approach to generate samples from conditional
belief functions for a restricted but non trivial subset of conditional belief
functions. It assumes the factorization (decomposition) of a belief function
along a bayesian network structure. It applies general conditional belief
functions.
