Computational-workload based binarization and partition of qualitative Markov trees for belief combination

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2711, Issue , 2003, Pages 306-318

  Liu, Weiru ^a   Hong, Xin ^a   Adamson, Kenny ^a  

^a UNIVERSITY OF ULSTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CALCULATIONS; FUNCTIONS; MARKOV PROCESSES; MATHEMATICAL MODELS; MATHEMATICAL TRANSFORMATIONS; BINS; TREES (MATHEMATICS); UNCERTAINTY ANALYSIS;

APPROXIMATIONS; BELIEF FUNCTIONS; BINARY TREE; ALTERNATIVE ALGORITHMS; BELIEF FUNCTION; BINARIZATIONS; COMPUTATIONAL WORKLOAD; EVIDENCE COMBINATION; MARKOV TREES; OR-NETWORKS; PARALLEL ENVIRONMENT;

TREES (MATHEMATICS); BINARY TREES;

EID: 8344266841     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-45062-7_25     Document Type: Conference Paper

References (17)

1. Barnett, J.A.: Computational methods for a mathematical theory of evidence. Proc. of IJCAI'81, Vancouver, BC, 868-875
2. Cano, A., Moral, S., and Salmeron, A.: Penniless propagation in join trees. International Journal of Intelligent Systems, Vol. 15, 2000, 1027-1059
3. Dubois, D. and Prade, H.: Inference in possibilistic hypergraphs. Proc. of IPMU'90, 228-30
4. Gordon, J. and Shortliffe, E.H.: A method for managing evidential reasoning in a hierarchical hypothesis space. Artificial Intelligence, Vol. 26, 1985, 323-357
5. Liu, W., Hong, X., and Adamson, K.: Parallel implementation of evidence combination in qualitative Markov tress. Technical Report, University of Ulster
6. Madsen, A. and Jensen, F.: LAZY propagation: A junction tree inference algorithm based on lazy propagation. Artificial Intelligence, Vol. 113, 1999, 203-245
7. Maheshwari, P. and Shen, H.: An efficient clustering algorithm for partitioning parallel programs. Parallel Computing, Vol. 24, 1998, 893-909
8. Pearl J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Mateo, 1988
9. Shafer, G.: A mathematical theory of evidence. Princeton University Press, 1976
10. Shafer, G. and Logan, R.: Implementing Dempster's rule for hierarchical evidence. Artificial Intelligence, Vol. 33, 1987, 271-298
11. Shafer, G., Shenoy, P. and Mellouli, K.: Propagating belief functions in qualitative Markov trees. Int. J. of Approx. Reasoning. Vol. 1, 1987, 349-400
12. Shenoy, P.: Binary join trees. Proc. of UAI'96, 492-499
13. Shenoy, P.: Binary join trees for computing marginals in the Shenoy-Shafer architecture. Int. J. of Approx. Reasoning, Vol. 17, 1997, 239-263
14. Shenoy, P. and Shafer, G.: Propagating belief functions with local computations. IEEE Expert, Vol 1, 1986, 43-52
15. Tessem, B.: Approximations for efficient computation in the theory of evidence. Artificial Intelligence, Vol. 61, 1993, 315-329
16. Voorbraak, F.: A computationally efficient approximation of Dempster-Shafer theory. International Journal of Man-Machine Studies, Vol. 30, 1989, 525-536
17. Wilson, N.: A Monte-Carlo algorithm for Dempster-Shafer belief. Proc. of UAI'91, 414