An efficient approach for finding the MPE in belief networks
  Given a belief network with evidence, the task of finding the I most probable
explanations (MPE) in the belief network is that of identifying and ordering
the I most probable instantiations of the non-evidence nodes of the belief
network. Although many approaches have been proposed for solving this problem,
most work only for restricted topologies (i.e., singly connected belief
networks). In this paper, we will present a new approach for finding I MPEs in
an arbitrary belief network. First, we will present an algorithm for finding
the MPE in a belief network. Then, we will present a linear time algorithm for
finding the next MPE after finding the first MPE. And finally, we will discuss
the problem of finding the MPE for a subset of variables of a belief network,
and show that the problem can be efficiently solved by this approach.
