Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic inference in multiply connected belief networks using loop cutsets
International Journal of Approximate Reasoning
Fusion and propagation with multiple observations in belief networks
Artificial Intelligence
Plan simulation using Bayesian networks
CAIA '95 Proceedings of the 11th Conference on Artificial Intelligence for Applications
Fast belief update using order-of-magnitude probabilities
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Complexity of probabilistic reasoning in directed-path singly-connected Bayes networks
Artificial Intelligence
Maximum entropy models for word sense disambiguation
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
Fast belief update using order-of-magnitude probabilities
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Topological parameters for time-space tradeoff
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Probabilistic network models for word sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
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We present two algorithms for exact and approximate inference in causal networks. The first algorithm, dynamic conditioning, is a refinement of cutset conditioning that has linear complexity on some networks for which cutset conditioning is exponential. The second algorithm, B-conditioning, is an algorithm for approximate inference that allows one to trade-off the quality of approximations with the computation time. We also present some experimental results illustrating the properties of the proposed algorithms.