Reed College

Portland, Oregon, USA

8:45-10:15am

**Toward a Market Model for Bayesian Inference**

*D. Pennock and M. Wellman***A unifying framework for several probabilistic inference algorithms***R. Dechter***Computing upper and lower bounds on likelihoods in intractable networks***T. Jaakkola and M. Jordan*(Outstanding Student Paper Award)**Query DAGs: A practical paradigm for implementing belief-network inference***A. Darwiche and G. Provan*

10:30-12:00am

**MIDAS: An Influence Diagram for Management of Mildew in Winter Wheat***A. Jensen and F. Jensen***Optimal Factory Scheduling under Uncertainty using Stochastic Dominance A****P. Wurman and M. Wellman***Supply Restoration in Power Distribution Systems --- A Case Study in Integrating Model-Based Diagnosis and Repair Planning***S. Thiebaux, M. Cordier, O. Jehl, J. Krivine***Network Engineering for Complex Belief Networks***S. Mahoney and K. Laskey*

Real-world experiences with uncertain reasoning systems 12:00-12:45pm

2:00-3:40pm

**Context-Specific Independence in Bayesian Networks**

*C. Boutilier, N. Friedman, M. Goldszmidt, D. Koller***Binary Join Trees***P. Shenoy***Why is diagnosis using belief networks insensitive to imprecision in probabilities?***M. Henrion, M. Pradhan, K. Huang, B. del Favero, G. Provan, P. O'Rorke***On separation criterion and recovery algorithm for chain graphs***Milan Studeny*

3:40-4:00pm

**Inference Using Message Propagation and Topology Transformation in Vector Gaussian Continuous Networks**

*S. Alag and A. Agogino***Constraining Influence Diagram Structure by Generative Planning: An Application to the Optimization of Oil Spill Response**

*J. Agosta***An Alternative Markov Property for Chain Graphs**

*S. Andersson, D. Madigan, and M. Perlman***Object Recognition with Imperfect Perception and Redundant Description**

*C. Barrouil and J. Lemaire***A Sufficiently Fast Algorithm for Finding Close to Optimal Junction Trees**

*A. Becker and D. Geiger***Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network**

*D. Chickering and D. Heckerman***Independence with Lower and Upper Probabilities**

*L. Chrisman***Topological Parameters for Time-Space Tradeoff**

*R. Dechter***A Qualitative Markov Assumption and its Implications for Belief Change**

*N. Friedman and J. Halpern***A Probabilistic Model for Sensor Validation**

*P. Ibarguengoytia and L. Sucar***Bayesian Learning of Loglinear Models for Neural Connectivity**

*K. Laskey and L. Martignon***Geometric Implications of the Naive Bayes Assumption**

*M. Peot***Optimal Monte Carlo Estimation of Belief Network Inference**

*M. Pradhan and P. Dagum***A Discovery Algorithm for Directed Cyclic Graphs***Thomas Richardson***Real-Time Estimation of Bayesian Networks**

*R. Welch***Testing Implication of Probabilistic Dependencies**

*S.K.M. Wong*

**A Structurally and Temporally Extended Bayesian Belief Network Model: Definitions, Properties, and Modelling Techniques***C. Aliferis and G. Cooper***Identifying independencies in causal graphs with feedback***J. Pearl and R. Dechter***Topics in Decision-Theoretic Troubleshooting: Repair and Experiment***J. Breese and D. Heckerman***A Polynomial-Time Algorithm for Deciding Equivalence of Directed Cyclic Graphical Models**

*T. Richardson*(Outstanding Student Paper Award)

10:30-12:00pm

**A Measure of Decision Flexibility**

*R. Shachter and M. Mandelbaum***A Graph-Theoretic Analysis of Information Value***K. Poh and E. Horvitz***Sound Abstraction of Probabilistic Actions in The Constraint Mass Assignment Framework***A. Doan and P.Haddawy***Flexible Policy Construction by Information Refinement***M. Horsch and D. Poole*

12:00-12:45pm

**Generalized Qualitative Probability***D. Lehmann***Uncertain Inferences and Uncertain Conclusions***H. Kyburg, Jr.*

**Arguing for Decisions: A Qualitative Model of Decision Making***B. Bonet and H. Geffner*

**Defining Relative Likelihood in Partially Ordered Preferential Structures***J. Halpern*

3:40-4:00pm

**An Algorithm for Finding Minimum d-Separating Sets in Belief Networks**

*S. Acid and L. de Campos***Plan Development using Local Probabilistic Models**

*E. Atkins, E. Durfee, K. Shin***Entailment in Probability of Thresholded Generalizations**

*D. Bamber***Coping with the Limitations of Rational Inference in the Framework of Possibility Theory**

*S. Benferhat, D. Dubois, H. Prade***Decision-Analytic Approaches to Operational Decision Making: Application and Observation**

*T. Chavez***Learning Equivalence Classes of Bayesian Network Structures**

*D. Chickering***Propagation of 2-Monotone Lower Probabilities on an Undirected Graph***L. Chrisman***Quasi-Bayesian Strategies for Efficient Plan Generation: Application to the Planning to Observe Problem**

*F. Cozman and E. Krotkov***Some Experiments with Real-Time Decision Algorithms**

*B. D'Ambrosio and S. Burgess***An Evaluation of Structural Parameters for Probabilistic Reasoning: Results on Benchmark Circuits**

*Y. El Fattah and R. Dechter***Learning Bayesian Networks with Local Structure***N. Friedman M. Goldszmidt***Theoretical Foundations for Abstraction-Based Probabilistic Planning**

*V. Ha and P. Haddawy***Probabilistic Disjunctive Logic Programming***L. Ngo***A Framework for Decision-Theoretic Planning I: Combining the Situation Calculus, Conditional Plans, Probability and Utility**

*D. Poole***Coherent Knowledge Processing at Maximum Entropy by SPIRIT**

*W. Roedder and C. Meyer***Efficient Enumeration of Instantiations in Bayesian Networks**

*S. Srinivas and P. Nayak*

**Failing and Succeeding at Real-World Reasoning under Uncertainty: Reflections on Three Decades of Work**

*Peter Hart*

8:45-10:00am

**Belief Revision in the Possibilistic Setting with Uncertain Inputs***D. Dubois and H. Prade***Approximations for Decision Making in the Dempster-Shafer Theory of Evidence***M. Bauer***Possible World Partition Sequences: A Unifying Framework for Uncertain Reasoning***C. Teng*

10:15-11:45pm

**Asymptotic model selection for directed networks with hidden variables***D. Geiger, D. Heckerman, C. Meek*-
**On the Sample Complexity of Learning Bayesian Networks**

*N. Friedman and Z. Yakhini* **Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates***C. Boutilier***Critical Remarks on Single Link Search in Learning Belief Networks***Y. Xiang, S.K.M Wong, N. Cercone*

11:45-12:30pm

**Computational complexity reduction for BN2O networks using similarity of states***A. Kozlov and J. Singh***Sample-and-Accumulate Algorithms for Belief Updating in Bayes Networks***E. Santos Jr., S. Shimony, E. Williams*

**Tail Simulation in Bayesian Networks***E. Castillo, C. Solares, P. Gomez*

**Efficient Search-Based Inference for Noisy-OR Belief Networks: TopEpsilon***K. Huang and M. Henrion*

4:00-5:00pm

Oregon Convention Center

- KDD:
**Knowledge Discovery and Data Mining: Toward a Unifying Framework**

*U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth* - UAI:
**Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network**

*D. Chickering and D. Heckerman* - KDD:
**Clustering using Monte Carlo Cross-Validation**

*P. Smyth* - UAI:
**Learning Equivalence Classes of Bayesian Network Structures**

*D. Chickering*

- UAI:
**Learning Bayesian Networks with Local Structure***N. Friedman M. Goldszmidt* - KDD:
**Rethinking the Learning of Belief Network Probabilities**

*R. Musick* - UAI:
**Bayesian Learning of Loglinear Models for Neural Connectivity**

*K. Laskey and L. Martignon* - KDD:
**Harnessing Graphical Structure in Markov Chain Monte Carlo Learning***P. Stolorz*

If you have questions or comments about the UAI-96 program, contact
the UAI-96 Program Chairs: Eric
Horvitz and Finn
Jensen. For conference arrangements information, please contact
Steve
Hanks.