We discuss the integration of the expectation-maximization (EM) algorithm for maximum likelihood learning of Bayesian networks with belief propagation algorithms for approximate inference. Specifically we propose to combine the outer-loop step of convergent belief propagation algorithms with the M-step of the EM algorithm. This then yields an approximate EM algorithm that is essentially still d...

The paper presents a new method and the corresponding program of presentation of bayesian belief networks. The belief network can be viewed and updated via World Wide Web. Consistency checks are possible. Edge removal and insertion operations are done in anìntelligent way' that is corrections of valuations are carried out automatically in a user-friendly way. The corresponding program is implem...

Suermondt, H.J. and G.F. Cooper, Initialization for the method of conditioning in Bayesian belief networks (Research Note), Artificial Intelligence 50 (1991) 83-94. The method of conditioning allows us to use Pearl's probabilistic-inference algorithm in multiply connected belief networks by instantiating a subset of the nodes in the network, the loop cutset. To use the method of conditioning, w...

This paper compares information theoretic approaches to building Bayesian belief networks to perform market index prediction. We show that the automatic model building can be done efficiently using the 2 ! criteria rather than mutual information alone. We suggest that when the number of variables are small, and instantiations are small, this criteria is a straightforward way of determining cond...

Bayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. The...

Qualitative probabilistic networks (QPNs) [13] are an abstraction of in uence diagrams and Bayesian belief networks replacing numerical relations by qualitative in uences and synergies. To reason in a QPN is to nd the e ect of decision or new evidence on a variable of interest in terms of the sign of the change in belief (increase or decrease). We review our work on qualitative belief propagati...

Compiling Bayesian networks (BNs) to junction trees and performing belief propagation over them is among the most prominent approaches to computing posteriors in BNs. However, belief propagation over junction tree is known to be computationally intensive in the general case. Its complexity may increase dramatically with the connectivity and state space cardinality of Bayesian network nodes. In ...