| Mellouli, K. "On the Propagation of Beliefs in Networks Using the Dempster-Shafer Theory of Evi- dence." Ph.D. Dissertation and Working Paper No. 196, School of Business, University of Kansas, April 1988. |
....an optimal elimination ordering for Y Gamma Sigma. Eliminating all of the vertices not in Sigma according to those orders, leave the vertices in Sigma which, because the graph is complete, are all leaf vertices and can be eliminated in arbitrary order. This idea is discussed in Zhang[1988] and Mellouli[1987]. Bertel e and Brioschi[1972] discus this idea under the name final theorem because the nodes in Sigma can be placed last in the ordering. 4.3 Remarks Lauritzen and Spiegelhalter[1988] recommend using a procedure called maximum cardinality search, from a paper by Tarjan and Yannakakis[1984] ....
Mellouli, K. [1987]. On the propagation of beliefs in networks using the Dempster-Shafer theory of evidence, Ph.D.
....by G. Shafer 1993, but that book is apparently not yet published. 1. 6 Networks of Non probabilistic representations of Uncertainty (These involve mostly generalizations of point probability) Qualitative Networks: Wel90a] Wel90b] Wel90c] PM93] Par95] Belief Functions: Dem90] Kon86] Mel87] SS86] SSM87] SS90] Wil90] Xu91] ZHS88] WD94b] SSS95] Sri95] Convex Probabilities: BF91] CDM91] CMVL93] CDM93] FB93] Tes92] dCM95] Chr96] Previsions: Gol90] Second Order Distributions: Mus93] NK91] Generalized Axiomatizations: These generalize most of the ....
K. Mellouli. On the Propagation of Beliefs in Networks using the Dempster-Shafer Theory of Evidence. PhD thesis, School of Business, University of Kansas, 1987.
....m 1 (B) m 2 (C) for A # # 3.3 Vacuous Extension of Belief Functions Let X and Y be two sets of variables such that Y # X. Let m Y be a bba defined on the domain # Y of Y. The extension of m Y to #X , denoted m Y #X means that the information in m Y is extended to a larger frame X [4]: m Y #X (A #X Y ) m Y (A) for A # # Y m Y #X (B) 0 if B is not in the form A#X Y 3.4 Pignistic Transformation The decision making problem is solved in the TBM framework by using the pignistic probability function defined and fully explianed by [10] BetP (#) P A##,##A ....
Mellouli, K.: On the propagation of beliefs in network using the Dempster-Shafer theory of evidence. Ph.D dissertation University of Kansas Lawrence KS (1987)
....The vacuous belief function is a belief function that satisfies [26] m(#) 1 and m(A) 0, #A # #, A #= #. 12) Such bba quantifies the state of total ignorance since there is no support given to any strict subset of #. A categorical belief function is a belief function that satisfies [19]: m(A) 1 for some A # #, A #= # and m(B) 0, #B # #, B #= A. 13) Such function has a unique focal element A di#erent from the frame of discernment #. 6 . The certain belief function is a categorical belief function which focal element is a singleton. It represents a state of total ....
K. Mellouli, On the propagation of beliefs in networks using the DempsterShafer theory of evidence, Ph.D dissertation, School of business, University of Kansas, Lawrence, KS, 1987.
No context found.
Mellouli, K. "On the Propagation of Beliefs in Networks Using the Dempster-Shafer Theory of Evi- dence." Ph.D. Dissertation and Working Paper No. 196, School of Business, University of Kansas, April 1988.
No context found.
Mellou li, K. "On the Propagation of Beliefs in Networks Using the Dempster-Shafer Theory of Evidence. " Ph .D . D issertation and Working Paper No. 196, School of Business, Un iversity of Kansas, April 1988.
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