主讲人: UIC 郭海鹏博士
时 间: 2006.10.12，2:00 - 4:00 p.m.
地 点: 北京师范大学珠海分校励泽楼A103
摘 要: Bayesian Belief Network (BBN) is the currently dominant method for reasoning under uncertainty in Artificial
Intelligence. Inferences with BBNs are either optimization, or marginalization, or both on the joint probability space.
We demonstrate that the joint probability distribution of a BBN is a multifractal in its most general form - a random multinomial multifractal. With sufficient asymmetry in
individual prior and conditional probability distributions, the joint distribution is not only highly skewed, but also stochastically self-similar and has clusters of high-robability
instantiations at all scales. Inspired by the multifractal properties, a sampling-and-search algorithm for finding the Most Probable Explanation (MPE) in BBN is developed and tested.
The experimental result shows that these multifractal properties provide good heuristic for solving the NP-hard MPE problem.