Propagating Uncertainty in Bayesian Networks by Probabilistic Logic Sampling
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.
Keywords: Bayesian Belief Networks, Probabilistic Logic
PDF Link: /papers/86/p149-henrion.pdf
AUTHOR = "Max Henrion
TITLE = "Propagating Uncertainty in Bayesian Networks by Probabilistic Logic Sampling",
BOOKTITLE = "Uncertainty in Artificial Intelligence 2 Annual Conference on Uncertainty in Artificial Intelligence (UAI-86)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1986",
PAGES = "149--163"