scholarly journals Testing and Improving Local Adaptive Importance Sampling in LFJ Local-JT in Multiply Sectioned Bayesian Networks

2016 ◽  
Author(s):  
Dan Wu ◽  
Sonia Bhatti
Keyword(s):  
Bayesian Networks ◽  
Importance Sampling ◽  
Adaptive Importance Sampling ◽  
Multiply Sectioned Bayesian Networks

scholarly journals AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks

2000 ◽  
Vol 13 ◽  
pp. 155-188 ◽  
Author(s):  
J. Cheng ◽  
M. J. Druzdzel
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Importance Sampling ◽  
Network Models ◽  
Evidential Reasoning ◽  
The Other ◽  
Sampling Algorithm ◽  
Importance Function ◽  
Sampling Algorithms ◽  
Bayesian Network Models

Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung & Chang, 1989; Shachter & Peot, 1989) and self-importance sampling (Shachter & Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, & Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, & Druzdzel, 1997), with evidence as unlikely as 10^-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm.

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