An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network
Artificial Intelligence
2013-04-05 v1
Abstract
We analyzed the convergence properties of likelihood- weighting algorithms on a two-level, multiply connected, belief-network representation of the QMR knowledge base of internal medicine. Specifically, on two difficult diagnostic cases, we examined the effects of Markov blanket scoring, importance sampling, demonstrating that the Markov blanket scoring and self-importance sampling significantly improve the convergence of the simulation on our model.
Keywords
Cite
@article{arxiv.1304.1141,
title = {An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network},
author = {Michael Shwe and Gregory F. Cooper},
journal= {arXiv preprint arXiv:1304.1141},
year = {2013}
}
Comments
Appears in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI1990)