Date of Award
2013
Publication Type
Master Thesis
Degree Name
M.Sc.
Department
Computer Science
Keywords
Computer science
Supervisor
Dan Wu
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
Multiply Sectioned Bayesian Network (MSBN) provides a model for probabilistic reasoning in multi-agent systems. The exact inference is costly and difficult to be applied in the context of MSBNs. So the approximate inference is used as an alternative. Recently, for reasoning in MSBNs, LJF-based Local Adaptive Importance Sampler (LLAIS) has been developed for approximate reasoning in MSBNs. However, the prototype of LLAIS is tested on Alarm Network (37 nodes). But further testing on larger networks has not been reported. In this thesis, LLAIS algorithm is tested on three large networks namely Hailfinder (56 nodes), Win95pts (76 nodes) and PathFinder (109 nodes), to measure for its reliability and scalability. The experiments done show that LLAIS without parameters tuned shows good convergence for Hailfinder and Win95pts but not for Pathfinder network. However, when the parameters are tuned the algorithm shows considerable improvement in its accuracy for all the three networks tested.
Recommended Citation
Bhatti, Sonia, "Testing and improving local adaptive importance sampling in LJF local-JT in multiply sectioned Bayesian networks" (2013). Electronic Theses and Dissertations. 4725.
https://scholar.uwindsor.ca/etd/4725