Though the Bayesian network is one of methods for probabilistic inferences in the artificial intelligence, also probabilistic models in the image processing based on the Bayesian statistics are regarded as Bayesian networks[1, 2, 3]. As one of approximate algorithms for probabilistic inferences by using Bayesian networks, belief propagation has been investigated[4, 5, 6, 7]. Recently, the belie...

Title of dissertation: An Extended Bayesian Belief Network Model of Multi-agent Systems for Supply Chain Management Ye Chen, Doctor of Philosophy, 2001 Dissertation Directed by: Yun Peng, Associate Professor, Department of Computer Science and Electronic Engineering This dissertation develops a theoretical model, called an extended Bayesian Belief Network (eBBN), of a Multi-agent System for Sup...

We took an innovative approach to service level management for network enterprise systems by using integrated monitoring, diagnostics, and adaptation services in a service-oriented architecture. The autonomous diagnosis for trouble-shooting of web service interruptions is based on Bayesian network models. In this paper, we present our methods for building the diagnostic models. We focus on two ...

SUMMARY This paper addresses the problem of learning Bayesian belief networks (BBN) based on the minimum description length (MDL) principle. First, we give a formula of description length based on which the MDL-based procedure learns a BBN. Secondly, we point out that the difference between the MDL-based and Cooper and Herskovits procedures is essentially in the priors rather than in the approa...

Bayesian (also called Belief) Networks (BN) are a powerful knowledge representation and reasoning mechanism. BN represent events and causal relationships between them as conditional probabilities involving random variables. Given the values of a subset of these variables (evidence variables) BN can compute the probabilities of another subset of variables (query variables). BN can be created aut...

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...

The Factored Frontier (FF) algorithm is a simple approximate inference algorithm for Dynamic Bayesian Networks (DBNs). It is very similar to the fully factorized version of the Boyen-Koller (BK) algorithm, but in­ stead of doing an exact update at every step followed by marginalisation (projection), it always works with factored distributions. Hence it can be applied to models for which the exa...