ACM Computing Surveys (CSUR)
Parameter Learning in Object-Oriented Bayesian Networks
Annals of Mathematics and Artificial Intelligence
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
Fusion of domain knowledge with data for structural learning in object oriented domains
The Journal of Machine Learning Research
Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation
The Journal of Machine Learning Research
An Analysis of Bayesian Network Model-Approximation Techniques
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Intelligent fault inference for rotating flexible rotors using Bayesian belief network
Expert Systems with Applications: An International Journal
Pseudo-tree-based incomplete algorithm for distributed constraint optimization with quality bounds
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Using qualitative relationships for bounding probability distributions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Mixtures of truncated basis functions
International Journal of Approximate Reasoning
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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I propose a general framework for approximating Bayesian belief networks through model simplification by arc removal. Given an upper bound on the absolute error allowed on the prior and posterior probability distributions of the approximated network, a subset of arcs is removed, thereby speeding up probabilistic inference.