id: 06067322
dt: a
an: 06067322
au: Boukhris, Imen; Benferhat, Salem; Elouedi, Zied
ti: Representing belief function knowledge with graphical models.
so: Xiong, Hui (ed.) et al., Knowledge science, engineering and management. 5th
    international conference, KSEM 2011, Irvine, CA, USA, December 12‒14,
    2011. Proceedings. Berlin: Springer (ISBN 978-3-642-25974-6/pbk).
    Lecture Notes in Computer Science 7091. Lecture Notes in Artificial
    Intelligence, 233-245 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-25975-3_21
ab: Summary: Belief function theory is an appropriate framework to model
    different forms of knowledge including probabilistic knowledge. One
    simple and efficient way to reason under uncertainty, is the use of
    compact graphical models, namely directed acyclic graphs. Therefore
    naturally, a question crosses the mind: If we deal with Bayesian belief
    knowledge does the network collapse into a Bayesian network? This paper
    attempts to answer this question by analyzing different forms of belief
    function networks defined with conditional beliefs defined either with
    a unique conditional distribution for all parents or a single
    conditional distribution for each single parent. We also propose a new
    method for the deconditionalization process to compute the joint
    distribution.
rv: