Approximating Bayesian Belief Networks by Arc Removal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
An edge deletion semantics for belief propagation and its practical impact on approximation quality
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A benchmark diagnostic model generation system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
| Hi-index | 0.00 |
Two approaches have been used to perform approximate inference in Bayesian networks for which exact inference is infeasible: employing an approximation algorithm, or approximating the structure. In this article we compare two structure-approximation techniques, edge-deletion and approximate structure learning based on sub-sampling, in terms of relative accuracy and computational efficiency. Our empirical results indicate that edge-deletion techniques dominate the subsampling/induction strategy, in both accuracy and performance of generating the approximate network. We show, for several large Bayesian networks, how edge-deletion can create approximate networks with order-of-magnitude inference speedups and relatively little loss of accuracy.