An Importance Sampling Algorithm Based on Evidence Pre-propagation
AbstractPrecision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the Evidence Pre-propagation Importance Sampling algorithm (EPIS-BN), an importance sampling algorithm that computes an approximate importance function by the heuristic methods: loopy belief Propagation and e-cutoff. We tested the performance of e-cutoff on three large real Bayesian networks: ANDES , CPCS, and PATHFINDER. We observed that on each of these networks the EPIS-BN algorithm gives us a considerable improvement over the current state of the art algorithm, the AIS-BN algorithm. In addition, it avoids the costly learning stage of the AIS-BN algorithm.
AUTHOR = "Yuan Changhe and Druzdzel Marek",
TITLE = "An Importance Sampling Algorithm Based on Evidence Pre-propagation",
BOOKTITLE = "Proceedings of the 19th Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann Publishers",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "624--631"