Dr. Rina Dechter @ UCI

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COMPSCI 276 Fall 2007, Network-Based Reasoning - Bayesian Networks

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Readings

Dechter, R., "Bucket Elimination: A unifying framework for Reasoning."
David Larkin, Approximate Decomposition: A Method for Bounding Probabilistic and Deterministic Queries
Judea Pearl, Probabilistic Reasoning in Intelligent Systems. Heckerman & Breese, Causal Independence for Probability Assessment and Inference Using Bayesian Networks.
Boutilier, Friedman, Goldszmidt & Koller, Context-Specific Independence in Bayesian Networks.
Dechter, Bucket Elimination: A Unifying Framework for Probabilistic Inference. 1998.
Dechter, "AAAI98 tutorial on reasoning."
Heckerman, A Tutorial on Learning with Bayesian Networks.
Kjaerulff, dHugin: A Computational System for Dynamic Time-Sliced Bayesian Networks.
Pearl, Causation, Action and Counterfactuals.
Dechter, Mini-buckets: a general scheme for approximating inference. Extended report.
Darwiche, Recursive Conditioning: Any-space conditioning algorithm with treewidth-bounded complexity.
Darwiche, Any-space probabilistic inference.
Darwiche, On the role of partial differentiation in probabilistic inference.
Horvitz, Breese, Heckerman, Hovel & Rommelse. The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users.
Binder, Murphy, Russell. Space-efficient inference in dynamic probabilistic networks.
Russell, Binder, Koller, Kanazawa. Local learning in probabilistic networks with hidden variables.
Dugad & Desai. A Tutorial on Hidden Markov Models.
Friedman, Geiger, Goldszmidt. Bayesian Network Classifiers.
Dechter, R., El Fattah, Y., Topological Parameters For Time-Space Tradeoff
Gagliardi, F., Generalizing Variable Elimination In Bayesian Networks
Rish, I; Dechter, R, AAAI 2000 Tutorial