Propagating Uncertainty in Bayesian Networks by Probabilistic Logic Sampling
Abstract
Bayesian belief networks and influence diagrams are attractive approaches for representing uncertain expert knowledge in coherent probabilistic form. But current algorithms for propagating updates are either restricted to singly connected networks (Chow trees), as the scheme of Pearl and Kim, or they are liable to exponential complexity when dealing with multiply connected networks. Probabilistic logic sampling is a new scheme employing stochastic simulation which can make probabilistic inferences in large, multiply connected networks, with an arbitrary degree of precision controlled by the sample size. A prototype implementation, named Pulse, is illustrated, which provides efficient methods to estimate conditional probabilities, perform systematic sensitivity analysis, and compute evidence weights to explain inferences.
Cite
Text
Henrion. "Propagating Uncertainty in Bayesian Networks by Probabilistic Logic Sampling." Conference on Uncertainty in Artificial Intelligence, 1986. doi:10.1016/B978-0-444-70396-5.50019-4Markdown
[Henrion. "Propagating Uncertainty in Bayesian Networks by Probabilistic Logic Sampling." Conference on Uncertainty in Artificial Intelligence, 1986.](https://mlanthology.org/uai/1986/henrion1986uai-propagating/) doi:10.1016/B978-0-444-70396-5.50019-4BibTeX
@inproceedings{henrion1986uai-propagating,
title = {{Propagating Uncertainty in Bayesian Networks by Probabilistic Logic Sampling}},
author = {Henrion, Max},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1986},
pages = {149-164},
doi = {10.1016/B978-0-444-70396-5.50019-4},
url = {https://mlanthology.org/uai/1986/henrion1986uai-propagating/}
}