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Artificial Intelligence Resources
Bayesian Networks Bibliography

Table of Contents
Computational Complexity
Fielded Systems
General References, Tutorials, and Surveys
Knowledge Engineering and Maintenance
Knowledge Representation and Structure
Learning
Reasoning Algorithms
Research Applications
Tools/Software for Bayesian Networks
Abductive Reasoning
New for 1996

General References, Tutorials, and Surveys
[Almond 95] Almond, Russell G., Graphical Belief Modeling, Chapman and Hall, 1995.
[Charniak 91] Charniak, Eugene, "Bayesian Networks Without Tears," AI Magazine, 12(4), pp 50-63, 1991.
[Cox 96] Cox, D. R. and Wermuth, N., Multivariate Dependencies, Chapman and Hall, 1996.
[Heckerman 95] Heckerman, David, "A Tutorial on Learning with Bayesian Networks," Technical Report MSR-TR-95-06, Microsoft Research, 1995.
[Jordan 97] Jordan, Michael I. and Ghahramani, Zoubin and Jaakkola,Tommi S., "An Introduction to Variational Methods for Graphical Models," Technical Report.
[Kornfeld 91] Kornfeld, Ari, "Causal Diagrams: Clarifying Uncertainty," AI Expert, November 1991.
[Lauritzen 96] Lauritzen, S. L., Graphical Models, Oxford University Press, Oxford, 1996.
[Madigan 95] Madigan, David and York, J., "Bayesian Graphical Models for Discrete Data," International Statistical Review, 63, pp 215-232, 1995.
[Neapolitan 90] Neapolitan, R. E., Probabilistic Reasoning in Expert Systems: Theory and Algorithms, John Wiley and Sons, 1990.
[Pearl 88] Pearl, Judea, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Mateo, CA, 1988.
[Springer-Verlag 93] Springer-Verlag, Spirtes, P., Glymour, C. and Scheines, R., Causation, Prediction, and Search, New York, N.Y., 1993.
[Whittaker 90] Whittaker, J., Graphical Models in Applied Multivariate Statistics, Wiley: Chichester, 1990.

Computational Complexity
[Chickering 94] Chickering, David M., Geiger, Dan, and Heckerman, David, "Learning Bayesian Networks is NP-Hard," Technical Report MSR-TR-94-17, Microsoft Research, 1994.
[Cooper 87] Cooper, Gregory F., "Probabilistic Inference Using Belief Networks is NP-hard," Technical Report KSL-87-27, Medical Computer Science Group, Stanford University, 1987.
[Dagum 93] Dagum, Paul and Luby, Michael, "Approximating Probabilistic Inference in Bayesian Belief Networks is NP-hard," Artificial Intelligence, 60(1), pp 141-153, 1993.
[Shimony 94] Shimony, Solomon, E., "Finding MAPs for Belief Networks is NP-hard," Artificial Intelligence, 68, pp 399-410, 1994.
[Suermondt 90] Suermondt, H. J. and Cooper, C. F., "Probabilistic Inference in Multiply Connected Belief Networks Using Loop Cutsets," International Journal of Approximate Reasoning, 4, pp 283-306, 1990.

Fielded Systems
[Heckerman 88] Intellipath Heckerman, David E., "An Empirical Comparison of Three Inference Methods," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 158-169, 1988.
[Horvitz 95] Vista Horvitz, Eric and Barry, Matthew, "Display of Information for Time-Critical Decision Making," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 296-305, 1995.
[Morjaria 93] GEMS Morjaria, Mahesh A., Rink, F. John, Smith, William D., Klempner, Geoff, Burns, Clayton, and Stein, Jan, "Commercialization of EPRI's Generator Expert Monitoring System (GEMS)," Expert System Application for the Electric Power Industry, EPRI, Phoenix, AZ, 1993.

Knowledge Engineering and Maintenance
[Druzdzel 95] Druzdzel, Marek J. and van der Gaag, Linda C., "Elicitation of Probabilities for Belief Networks: Combining Qualitative and Quantitative Information," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 141-148, 1995.
[Henrion 87] Henrion, M. and Cooley, D.R., "An Experimental Comparison of Knowledge Engineering for Expert Systems and for Decision Analysis," Proceedings of the National Conference on Artificial Intelligence, Morgan Kaufmann, San Mateo, CA, pp 471-476, 1987.
[Horvitz 88] Horvitz, E. J., Breese, J. S., and Henrion, M., "Decision Theory in Expert Systems and Artificial Intelligence," International Journal of Approximate Reasoning, 2, pp 247-302, 1988.
[Jensen 95] Jensen, Finn V., "Cautious propagation in Bayesian networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 323-328, 1995.
[Kim 95] Kim, Young-Gyun and Valtorta, Marco, "On the Detection of Conflicts in Diagnostic Bayesian Networks Using Abstraction," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 362-367, 1995.
[Madigan 94] Madigan, David, Gavrin, Jonathan, and Raftery, Adrian E., "Eliciting Prior Information to Enhance the Predictive Performance of Bayesian Graphical Models," Technical Report 270, Department of Statistics, University of Washington, 1994. Also in Communications in Statistics - Theory and Methods, 24, pp 2271-2292, 1995.
[Pearl 95] Pearl, Judea, "On the Testability of Causal Models with Latent and Instrumental Variables," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 435-443, 1995.
[Pradhan 95] Pradhan, M., Henrion, M., Provan, G., Favero, B. D., and Huang, K., "The Sensitivity of Belief Networks to Imprecise Probabilities: An Experimental Investigation," Technical Report KSL-95-66, Knowledge Systems Laboratory, Medical Computer Science, 1995.
[Provan 95] Provan, Gregory, "Abstraction in Belief Networks: The Role of Intermediate States in Diagnostic Reasoning," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 464-471, 1995.

Knowledge Representation and Structure
[Azevedo-Filho 94] Azevedo-Filho, Adriano and Shachter, Ross D., "Laplace's Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 28-36, 1994.
[Balke 94] Balke, Alexander and Pearl, Judea, "Counterfactual Probabilities: Computational Methods, Bounds and Applications," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 46-54, 1994.
[Balke 95] Balke, Alexander and Pearl, Judea, "Counterfactuals and Policy Analysis in Structural Models," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 11-18, 1995.
[Chickering 95] Chickering, David Maxwell, "A Transformational Characterization of Equivalent Bayesian Network Structures," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 87-98, 1995.
[Darwiche 94] Darwiche, Adnan and Goldszmidt, Mois\'{e}s, "Action Networks: A Framework for Reasoning about Actions and Change Under Uncertainty," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 136-144, 1994.
[Driver 95] Driver, Eric and Morrell, Darryl, "Implementation of Continuous Bayesian Networks Using Sums of Weighted Gaussians," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 134-140, 1995.
[Hanks 95] Hanks, Steve, Madigan, David, and Gavrin, Jonathan, "Probabilistic Temporal Reasoning with Endogenous Change," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 245-254, 1995.
[Heckerman 91] Heckerman, David, Probabilistic Similarity Networks, The MIT Press, 1991.
[Heckerman 94b] Heckerman, David, "Causal Independence for Probability Assessment and Inference Using Bayesian Networks," Technical Report MSR-TR-94-08, Microsoft Research, 1994.
[Jenzarli 95] Jenzarli, Ali, "Information/Relevance Influence Diagrams," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 329-337, 1995.
[Meek 95] Meek, Christopher, "Strong completeness and faithfulness in Bayesian networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 411-418, 1995.
[Parsons 95] Parsons, Simon, "Refining Reasoning in Qualitative Probabilistic Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 427-434, 1995.
[Tucci 95] Tucci,Robet R., "Quantum Bayesian Nets," Int. Journal of Mod. Phys. B9, pp 295-337, 1995.
[Santos 96] Santos, Eugene Jr., "On Linear Potential Functions for Approximating Bayesian Computations," Journal of the ACM, 43(3), pp 399-403, 1996.
[Shimony 96] Shimony, Solomon Eyal and Santos, Eugene, Jr., "Exploiting Case-Based Independence for Approximating Marginal Probabilities," International Journal of Approximate Reasoning, 14(1), 25-54, 1996.
[Parsons 95] Spirtes, Peter, "Directed Cyclic Graphical Representations of Feedback Models," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 491-498, 1995.
[Xiang 95] Xiang, Y., "Optimization of Inter-Subnet Belief Updating in Multiply Sectioned Bayesian Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 565-573, 1995.
[Young 96] Young, Joel D. and Santos, Eugene, Jr., "Introduction to Temporal Bayesian Networks," Proceedings of the Midwest Conference on Artificial Intelligence and Cognitive Science, 1996.

Learning
[Aliferis 94] Aliferis, Constantin F. and Cooper, Gregory F., "An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief Networks Using Simulated Data Sets," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 8-14, 1994.
[Ayers 94] Ayers, Derek D., "A Bayesian Method Reexamined," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 23-27, 1994.
[Bouckaert 94] Bouckaert, Remco R., "Properties of Bayesian Belief Network Learning Algorithms," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 102-109, 1994.
[Buntine 95] Buntine, Wray L, "Chain Graphs for Learning," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 46-54, 1995.
[Geiger 95] Geiger, Dan and Heckerman, David, "A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 196-207, 1995.
[Heckerman 94] Heckerman, David and Geiger, Dan, "Learning Bayesian Networks," Technical Report MSR-TR-95-02, Microsoft Research, 1994.
[Lam 94] Lam, Wai and Bacchus, Fahiem, "Learning Bayesian Belief Networks: An Approach based on the MDL Principle," Computation Intelligence, 10, pp 269-293, 1994.
[Madigan 91] Madigan, David and Raftery, Adrian E., "Model Selection and Accounting for Model Uncertainty in Graphical Models using Occam's Window," Technical Report 213, Department of Statistics, University of Washington, 1991. Also in Journal of American Statistical Assocation, 89, pp 1535-1546, 1994.
[Madigan 96] Madigan, David and Almond, Russell G., "On Test Selection Strategies for Belief Networks," in Learning from Data: Artificial Intelligence and Statistics V, D. D. Fisher and H. Lenz (Eds.), Springer Verlag, to appear.

Reasoning Algorithms
[Becker 94] Becker, Ann and Geiger, Dan, "Approximation Algorithms for the Loop Cutset Problem," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 60-68, 1994.
[Bouckaert 94b] Bouckaert, Remco R., "A Stratified Simulation Scheme for Inference in Bayesian Belief Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 110-117, 1994.
[Castillo 95] Castillo, Enrique F., Bouckaert, Remco R., Sarabia, José, and Solares, Cristina, "Error Estimation in Approximate Bayesian Belief Network Inference," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 55-62, 1995.
[Charniak 92] Charniak, Eugene and Santos, Eugene Jr., "Dynamic MAP Calculations for Abduction," Proceedings of the National Conference on Artificial Intelligence, AAAI Press, Menlo Park, CA, pp 552-557, 1992.
[Charniak 94] Charniak, Eugene and Shimony, Solomon E., "Cost-Based Abduction and MAP Explanation," Artificial Intelligence, 66, pp 345-374, 1994.
[Cousins 91] Cousins, Steve B., Chen, William, and Frisse, Mark E., "CaBeN: A Collection of Algorithms for Belief Networks," Technical Report WUCS-91-25, Department of Computer Science, Washington University, St.~Louis, 1991.
[D'Ambrosio 94] D'Ambrosio, Bruce, "SPI in Large BN2O Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 128-135, 1994.
[Darwiche 95] Darwiche, Adnan, "Conditioning Algorithms for Exact and Approximate Inference in Causal Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 99-107, 1995.
[Delcher 95] Delcher, Arthur L, Grove, Adam, Kasif, Simon, Pearl, Judea, "Logarithmic-Time Updates and Queries in Probabilistic Network," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 116-124, 1995.
[Diez 96] Diez, F. J., "Local Conditioning in Bayesian Networks," Artificial Intelligence, to appear, 1996.
[Draper 95] Draper, Denise L., "Clustering Without (Thinking About) Triangulation," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 125-133, 1995.
[Goldszmidt 95] Goldszmidt, Moisés, "Fast Belief Update Using Order-of-Magnitude Probabilities," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 208-216, 1995.
[Horvitz 89] Horvitz, Eric J., Suermondt, J. Jacques, and Cooper, Gregory F., "Bounded Conditioning: Flexible Inference for Decisions Under Scarce Resources," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, 1989.
[Hulme 95] Hulme, Mark, "Improved Sampling for Diagnostic Reasoning in Bayesian Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 315-322, 1995.
[Jensen 95b] Jensen, Claus S., Kong, Augustine, and Kjaerulff, Uffe, "Blocking Gibbs Sampling in Very Large Probabilistic Expert Systems," Journal of Human-Computer Studies, 42, pp 647-666, 1995.
[Kanazawa 95] Kanazawa, Keiji, Koller, Daphne, and Russell, Stuart, "Stochastic simulation algorithms for dynamic probabilistic networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 346-351, 1995.
[Kjaerulff 95] Kjaerulff, Uffe, "HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 368-375, 1995.
[Lauritzen 88] Lauritzen, S. L. and Spiegelhalter, D. J., "Local Computations with Probabilities on Graphical Structures and Their Applications to Expert Systems," J. Royal Statistical Society, 50(2), pp 157-224, 1988.
[Li 94] Li, Zhaoyu and D'Ambrosio, Bruce, "Efficient Inference in Bayes Nets as a Combinatorial Optimization Problem," International Journal of Approximate Reasoning, 11(1), pp 55-81, 1994.
[Rojas 93] Rojas-Guzman, Carlos and Kramer, Mark A., "GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 368-375, 1993.
[Santos 93] Santos, Eugene Santos Jr., "A Fast Hill-Climbing Approach Without An Energy Function for Finding MPE," Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, IEEE Press, 1993.
[Santos 94b] Santos, Eugene Santos Jr. and Shimony, Solomon E., "Belief Updating by Enumerating High-Probability Independence-Based Assignments," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 506-513, 1994.
[Shacters 89] Shachter, Ross D. and Peot, Mark A., "Simulation Approaches to General Probabilistic Inference on Belief Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, 1989.
[Suermondt 90] Suermondt, H. J. and Cooper, G. F., "Probabilistic Inference in Multiply Connected Brief Networks Using Loop Cutsets," International Journal of Approximate Reasoning, 4(4), pp 283-306, 1990.
[Sy 92] Sy, Bon K., "Reasoning MPE to Multiply Connected Belief Networks Using Message Passing," Proceedings of the National Conference on Artificial Intelligence, AAAI Press, Menlo Park, CA, pp 570-576, 1992.

Research Applications
[Anrig et al. 97] Anrig, B., R. Haenni, J. Kohlas and N. Lehmann, "Assumption-based Modeling using ABEL," First International Joint Conference on Qualitative and Quantitative Practical Reasoning; ECSQARU--FAPR'97, 1997
[Bickmore 94] Bickmore, Timothy W., "Real-Time Sensor Data Validation," NASA Contractor Report 195295, National Aeronatics and Space Administration, April, 1994
[Blythe 94] Blythe, Jim, "Planning with External Events," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 94-101, 1994.
[Breese 95] Breese, John S. and Blake, Russ, "Automating Computer Bottleneck Detection with Belief Nets," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 36-45, 1995.
[Ezawa 95] Ezawa, Kazuo J. and Schuermann, Til, "Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures," Computer Bottleneck Detection with Belief Nets," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 157-166, 1995.
[Haddawy 94] Haddawy, P., Jacobson, J., and Kahn, C. E. Jr, "An Educational Tool For High-Level Interaction With Bayesian Networks," Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, IEEE Computer Society Press, Los Alamitos, CA, pp 578-584, 1994.
[Huang 94] Huang, T., Koller, D., Malik, J., Ogasawara, G., Rao, B., Russell, S., and Weber, J., "Automatic Symbolic Traffic Scene Analysis Using Belief Networks," Proceedings of National Conference on Artificial Intelligence, Morgan Kaufmann, San Mateo, CA, pp ???-???, 1994.
[Kahn 95] Kahn, C. E. Jr, Roberts, L. M., Wang, K., Jenks, D., and Haddawy, P., "Preliminary Investigation of a Bayesian Network for Mammographic Diagnosis of B Breast Cancer," Proceedings of the 19th Annual Symposium on Computer Applications in Medical Care, Hanley & Belfus, Philadelphia, PA, pp 208-212, 1995.
[Kirman 93] Kirman, Jak, Nicholson, Ann, Lejter, Moises, Dean, Thomas, and Santos, Eugene, Jr., "Using Goals to Find Plans with High Expected Utility," Proceedings of the Second European Workshop on Planning, Linkoping, Sweden, pp 158-170, 1993.
[Klingler 94] Klingler, Tom M. and Brutlag, Douglas L., "Discovering Structural Correlations in alpha-Helices," Protein Science, 3, pp 1847-1857, 1994.
[Madigan 94b] Madigan, David, Mosurski, Krzysztof, and Almond, Russell G., "Explanation in Belief Networks," to appear in Journal of Computational and Graphical Statistics, 1996.
[Saffiotti 91] Saffiotti, A. and Umkehrer, E., "PULCINELLA: A General Tool for Propagating Uncertainty in Valuation Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, 1991.

Tools/Software for Bayesian Networks
[HsuJo 03] Hsu, W. H. and Joehanes, R. J. (2003). Bayesian Network tools in Java. Software demonstration, Conference on Uncertainty in Artificial Intelligence (UAI-2003). Acapulco, Mexico, August, 2003.
[Ngo 95] Ngo, L., Haddawy, P., Helwig, J., "A Theoretical Framework for Context-Sensitive Temporal Probability Model Construction with Application to Plan Projection," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 419-426, 1995.
[Srinivas 90] Srinivas, S . and Breese, J, "IDEAL: A Software Package for Analysis of Influence Diagrams," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp ???-???, 1990.

Abductive Reasoning
[Charniak 94] Charniak, Eugene and Shimony, Solomon E., "Cost-Based Abduction and MAP Explanation," Artificial Intelligence, 66, pp 345-374, 1994.
[Hobbs 88] Hobbs, Jerry R., Stickel, Mark, Martin, Paul, and Edwards, Douglas, "Interpretation as Abduction," Proceedings of the 26th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Menlo Park, CA, pp 95-103, 1988.
[Peng 90] Peng, Yun and Reggia, James A., Abductive Inference Models for Diagnostic Problem-Solving, Springer-Verlag, 1990.
[Santos 94] Santos, Eugene Jr., "A Linear Constraint Satisfaction Approach to Cost-Based Abduction," Artificial Intelligence, 65(1), pp 1-28, 1994.
[Shanahan 89] Shanahan, Murray, "Prediction is Deduction but Explanation is Abduction," Proceedings of the International Joint Conference on Artificial Intelligene, Margan Kaufmann, San Mateo, CA, pp 1055-1060, 1989.

New Additions for 1996
[Acid 96] Acid, Silvia, M. de Campos, Luis, "An Algorithm for Finding Minimum d-Seperating Sets in Belief Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 3-10, 1996.
[Agosta 96] Agosta, John M., "Constraining Influence Diagram Structure by Generative Planning: An Application to the Optimization of Oil Spill Response," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 11-19, 1996.
[Alag 96] Alag, Satnam, Agogino, Alice M., "Inference Using Message Propagation and Topology Transformation in Vector Gaussian Continuous Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 20-27, 1996.
[Aliferis 96] Aliferis, Constantin F., Cooper, Gregory F., "A Structurally and Temporally Extended Bayesian Belief Network Model: Definitions, Properties, and Modeling Techniques," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 28-39, 1996.
[Andersson 96] Andersson, Steen A., Madigan, David, and Perlman, Michael D., "An Alternative Markov Property for Chain Graphs," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 40-48, 1996.
[Atkins 96] Atkins, Ella M., Durfee, Edmund H., and Shin, Kang G., "Plan Development using Local Probabilistic Models," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 49-56, 1996.
[Bamber 96] Bamber, Donald, "Entailment in Probability of Thresholded Generalizations," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 57-64, 1996.
[Barrouil 96] Barrouil, Claude, Lemaire, Jerome, "Object Recognition with imperfect Perception and Redundant Description," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 65-72, 1996.
[Bauer 96] Bauer, Mathias, "Approximations for Decision Making in the Dempster-Shafer Theory of Evidence," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 73-80, 1996.
[Becker 96] Becker, Ann, Geiger, Dan, "A sufficiently fast algorithm for finding close to optimal junction trees," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 81-89, 1996.
[Benferhat 96] Benferhat, Salem, Dudois, Didier, and Prade, Henri, "Coping with the Limitations of Rational Inference in the Framework of Possibility Theory," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 90-97, 1996.
[Bonet 96] Bonet, Blai, Geffner, Hector, "Arguing for Decisions: A Qualitative Model of Decision Making," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 98-105, 1996.
[Boutilier 96a] Boutilier, Craig, "Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 106-114, 1996.
[Boutilier 96b] Boutilier, Craig, Friedman, Nir, Goldszmidt, Moises, and Koller, Daphne, "Context-Specific Independence in Bayesian Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 115-123, 1996.
[Breese 96] Breese, John S., Heckerman, David, "Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 124-132, 1996.
[Castillo 96] Castillo, Enrique F., Solares, Cristina, and Gomez, Patricia, "Tail Sensitivity Analysis in Bayesian Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 133-140, 1996.
[Chavez 96] Chavez, Tom, "Decision-Analytic Approaches to Operational Decision Making: Application and Observation," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 141-149, 1996.
[Chickering 96a] Chickering, David M., "Learning Equivalence Classes of Bayesian Network Structures," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 150-157, 1996.
[Chickering 96b] Chickering, David M., Heckerman, David, "Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 158-168, 1996.
[Chrisman 96a] Chrisman, Lonnie, "Independence with Lower and Upper Probabilities," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 169-177, 1996.
[Chrisman 96b] Chrisman, Lonnie, "Propagation of 2-Monotone Lower Probabilities on an Undirected Graph," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 178-185, 1996.
[Cozman 96] Cozman, Fabio, Krotkov, Eric, "Quasi-Bayesian Strategies for Efficient Plan Generation: Application to the Planning to Observe Problem," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 186-193, 1996.
[D'Ambrosio 96] D'Ambrosio, Bruce, Burgess, Scott, "Some Experiments with Real-time Decision Algorithms," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 194-202, 1996.
[Darwiche 96] Darwiche, Adnan, Provan, Gregory, "Query DAGs: A practicle paradigm for implementing belief-network inference," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 203-210, 1996.
[Dechter 96a] Dechter, Rina, "Bucket elimination: A unifying framework for probabilistic inference," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 211-219, 1996.
[Dechter 96b] Dechter, Rina, "Topological parameters for time-space tradeoff," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 220-227, 1996.
[Doan 96] Doan, AnHai, Haddawy, Peter, "Sound Abstraction of Probabilistic Actions in The Constraint Mass Assignment Framework," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 228-235, 1996.
[Dubois 96] Dubois, Didier, Prade, Henri, "Belief Revision with Uncertain Imputs in the Possibilistic Setting," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 236-243, 1996.
[Fattah 96] Fattah, Yousri El, Dechter, Rina, "An evaluation of structural parameters for probabilistic reasoning: Results on benchmark circuits," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 244-251, 1996.
[Friedman 96a] Friedman, Nir, Goldszmidt, Moises, "Learning Bayesian Networks with Local Structure," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 252-262, 1996.
[Friedman 96b] Friedman, Nir, Halpern, Joseph Y., "A Qualitative Markov Assumption and Its Implications for Belief Change," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 263-273, 1996.
[Friedman 96c] Friedman, Nir, Yakhini, Zohar, "On the Sample Complexity of Learning Bayesian Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 274-282, 1996.
[Geiger 96] Geiger, Dan, Heckerman, David, Meek, Christopher, "Asymptotic Model Selection for Directed Networks with Hidden Variables," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 283-290, 1996.
[Ha 96] Ha, Vu, Haddawy, Peter, "Theoretical Foundations for Abstraction-Bases Probabilistic Planning," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 291-298, 1996.
[Halpern 96] Halpern, Joseph Y., "Defining Relative Likelihood in Partially-Ordered Preferential Structures," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 299-306, 1996.
[Henrion 96] Henrion, Max, Pradhan, Malcolm, Del Favero, Brendan, Huang, Kurt, Provan, Gregory, and O'Rorke, Paul, "Why is diagnosis using belief networks insensitive to imprecision in probabilities," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 307-314, 1996.
[Horsch 96] Horsch, Michael C., Poole, David, "Flexible Policy Construction by Information Refinement," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 315-324, 1996.
[Huang 96] Huang, Kurt, Henrion, Max, "Efficient Search-Based Inference for Noisy-OR Belief Networks: TopEpsilon," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 325-331, 1996.
[Ibarguengoytia 96] Ibarguengoytia, P. H., Sucar, L. E., and Vanera, S., "A Probabilistic Model for Sensor Validation," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 332-339, 1996.
[Jaakkola 96] Jaakkola, Tommi S., Jordan, Michael I., "Computing upper and lower bounds on likelihoods in intractable networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 340-348, 1996.
[Jensen 96] Jensen, Allan L., Jensen, Finn V., "MIDAS- An Influence Diagram for Management of Mildew in Winter Wheat," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 349-356, 1996.
[Kozlov 96] Kozlov, Alexander V., Singh, Jaswinder P., "Computational complexity reduction for BN2O networks using similarity of states," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 357-364, 1996.
[Kyburg 96] Kyburg, Henry E., Jr., "Uncertain Inferences and Uncertain Conclusions," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 365-372, 1996.
[Laskey 96] Laskey, Kathryn B., Martignon, Laura, "Bayesian Learning of Loglinear Models for Neural Connectivity," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 373-380, 1996.
[Lehmann 96] Lehmann, Daniel, "Generalized Qualitative Probability: Savage revisted," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 381-388, 1996.
[Mahoney 96] Mahoney, Suzanne M., Laskey, Kathryn B., "Network Engineering for Complex Belief Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 389-396, 1996.
[Ngo 96] Ngo, Liem, "Probabilistic Disjunctive Logic Programming," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 397-404, 1996.
[Pennock 96] Pennock, David M., Wellman, Michael P., "Toward a Market Model for Bayesian Inference," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 405-413, 1996.
[Peot 96] Peot, Mark A., "Geometric Implications of the Naive Bayes Assumption," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 414-419, 1996.
[Pearl 96] Pearl, Judea, Dechter, Rina, "Identifying Independencies inb Casual Graphs with Feedback," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 420-426, 1996.
[Poh 96] Poh, Kim Leng, Horvitz, Eric, "A Graph-Theortic Analysis of Information Value," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 427-435, 1996.
[Poole 96] Poole, David, "A Framework for Decision-Theoretic Planning I: Combining the Situation Calculus, Conditional Plans, Probability and Utility," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 436-445, 1996.
[Pradhan 96] Pradhan, Malcolm, Dagum, Paul, "Optimal Monte Carlo Estimation of Belief Network Inference," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 446-453, 1996.
[Richardson 96a] Richardson, Thomas, "A Discovery Algorithm for Directed Cyclic Graphs," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 454-461, 1996.
[Richardson 96b] Richardson, Thomas, "A Polynomial-Time Algorithm for Deciding Equivalence of Directed Cyclic Graphical Models," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 462-469, 1996.
[Rodder 96] Rodder, Wilhelm, Meyer, Carl-Heinz, "Coherent Knowledge Processing at Maximum Entropy by SPIRIT," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 470-476, 1996.
[Santos 96] Santos, Eugene, Jr., Shimony, Soloman Eyal, and Williams, Edward, "Sample-and-Accumulate Algorithms for Belief Updating in Bayes Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 477-484, 1996.
[Shachter 96] Shachter, Ross D., Mandelbaum, Marvin, "A Measure of Decision Flexibility," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 485-491, 1996.
[Shenoy 96] Shenoy, Prakash P., "Binary Join Trees," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 492-499, 1996.
[Srinivas 96] Srinivas, Sampath, Nayak, Pandurang, "Efficient enumeration of instantiations in Bayesian networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 500-508, 1996.
[Studeny 96] Studeny, Milan, "On Separation Criterion and Recovery Algorithm for Chain Graphs," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 509-516, 1996.
[Teng 96] Teng, Choh Man, "Possible World Partition Sequences: A Unifying Framework for Uncertain Reasoning," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 517-524, 1996.
[Thiebaux 96] Thiebaux, Sylvie, Cordier, Marie-Odile, Jehl, Olivier, and Krivine, Jean-Paul, " Supply Restoration in Power Distribution Systems- A Case Study in Integrating Model-Bases Diagnosis and Repair Planning," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 525-532, 1996.
[Welch 96] Welch, Robert L., "Real Time Estimation of Bayesian Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 533-544, 1996.
[Wong 96] Wong, S. K. M., "Testing Implication of Probabilistic Dependencies," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 545-553, 1996.
[Wurman 96] Wurman, Peter R., Wellman, Michael P., "Optimal Factory scheduling using Stochastic Dominance A*," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 554-563, 1996.
[Xiang 96] Xiang, Y., Wong, S. K. M., and Cercone, N., "Critical Remarks on Single Link Search in Learning Belief Networks," Proceedings of the Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp 564-571, 1996.

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