DocumentCode
420296
Title
Granular jointree probability propagation
Author
Butz, C.J. ; Lingras, P.
Author_Institution
Dept. of Comput. Sci., Regina Univ., Sask., Canada
Volume
1
fYear
2004
fDate
27-30 June 2004
Firstpage
69
Abstract
Jointree computation continues to be central to the theory and practice of probabilistic expert systems. Recent research has incorporated granular structures to facilitate propagation in the jointree. In this paper, we propose a method for granular jointree probability propagation. Our method extends the previous works by allowing the granular levels to communicate with each other. It is explicitly demonstrated that our granular approach increases the amount of parallelism during probability propagation.
Keywords
Markov processes; belief networks; expert systems; probabilistic logic; probability; trees (mathematics); Bayesian network; directed acyclic graph; granular jointree probability propagation; granular structures; hierarchical Markov network; jointree computation; parallel computation; probabilistic expert systems; Bayesian methods; Computer science; Concurrent computing; Expert systems; Inference algorithms; Markov random fields; Parallel processing; Probability distribution;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Information, 2004. Processing NAFIPS '04. IEEE Annual Meeting of the
Print_ISBN
0-7803-8376-1
Type
conf
DOI
10.1109/NAFIPS.2004.1336251
Filename
1336251
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