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K. S. Jasmine
RV College of Engineering
Gavani Prathviraj S.
RV College of Engineering
P Ijantakar Rajashekar
RV College of Engineering
K. A. Sumithra Devi
RV College of Engineering
Vol. 2 No. 3 (2013), Articles, pages 01-07


Over the time in computational history, belief networks have become an increasingly popular mechanism for dealing with uncertainty in systems. It is known that identifying the probability values of belief network nodes given a set of evidence is not amenable in general. Many different simulation algorithms for approximating solution to this problem have been proposed and implemented. This paper details the implementation of such algorithms, in particular the two algorithms of the belief networks namely Logic sampling and the likelihood weighing are discussed. A detailed description of the algorithm is given with observed results. These algorithms play crucial roles in dynamic decision making in any situation of uncertainty.


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