12. UAI 1996: Portland, Oregon, USA

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

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