14. UAI 1998: Madison, Wisconsin, USA

• Leila Amgoud, Claudette Cayrol:
  On the Acceptability of Arguments in Preference-based Argumentation. 1-7
  
• Salem Benferhat, Claudio Sossai:
  Merging uncertain knowledge bases in a possibilistic logic framework. 8-15
  
• Mark Bloemeke, Marco Valtorta:
  A Hybrid Algorithm to Compute Marginal and Joint Beliefs in Bayesian Networks and Its Complexity. 16-23
  
• Craig Boutilier, Ronen I. Brafman, Christopher W. Geib:
  Structured Reachability Analysis for Markov Decision Processes. 24-32
  
• Xavier Boyen, Daphne Koller:
  Tractable Inference for Complex Stochastic Processes. 33-42
  
• John S. Breese, David Heckerman, Carl Myers Kadie:
  Empirical Analysis of Predictive Algorithms for Collaborative Filtering. 43-52
  
• Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete:
  Query Expansion in Information Retrieval Systems using a Bayesian Network-Based Thesaurus. 53-60
  
• Charles Castel, Corine Cossart, Catherine Tessier:
  Dealing with Uncertainty in Situation Assessment: towards a Symbolic Approach. 61-68
  
• Enrique F. Castillo, Juan M. Fernández-Luna, Pilar Sanmartin:
  Marginalizing in Undirected Graph and Hypergraph Models. 69-78
  
• Urszula Chajewska, Lise Getoor, Joseph Norman, Yuval Shahar:
  Utility Elicitation as a Classification Problem. 79-88
  
• Fabio Gagliardi Cozman:
  Irrelevance and Independence Relations in quasi-Bayesian Networks. 89-96
  
• Adnan Darwiche:
  Dynamic Jointrees. 97-104
  
• Benoit Desjardins:
  On the semi-Markov Equivalence of Causal Models. 105-112
  
• Didier Dubois, Hélène Fargier, Henri Prade:
  Comparative uncertainty, belief functions and accepted beliefs. 113-120
  
• Didier Dubois, Henri Prade, Régis Sabbadin:
  Qualitative Decision Theory with Sugeno Integrals. 121-128
  
• Nir Friedman:
  The Bayesian Structural EM Algorithm. 129-138
  
• Nir Friedman, Kevin P. Murphy, Stuart J. Russell:
  Learning the Structure of Dynamic Probabilistic Networks. 139-147
  
• Alexander Gammerman, Katy S. Azoury, Vladimir Vapnik:
  Learning by Transduction. 148-155
  
• Dan Geiger:
  Graphical Models and Exponential Families. 156-165
  
• Clark Glymour:
  Psychological and Normative Theories of Causal Power and the Probabilities of Causes. 166-172
  
• Adam J. Grove, Joseph Y. Halpern:
  Updating Sets of Probabilities. 173-182
  
• Peter Grünwald, Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri:
  Minimum Encoding Approaches for Predictive Modeling. 183-192
  
• Vu A. Ha, Peter Haddawy:
  Toward Case-Based Preference Elicitation: Similarity Measures on Preference Structures. 193-201
  
• Joseph Y. Halpern:
  Axiomatizing Causal Reasoning. 202-210
  
• Eric A. Hansen:
  Solving POMDPs by Searching in Policy Space. 211-219
  
• Milos Hauskrecht, Nicolas Meuleau, Leslie Pack Kaelbling, Thomas L. Dean, Craig Boutilier:
  Hierarchical Solution of Markov Decision Processes using Macro-actions. 220-229
  
• David Heckerman, Eric Horvitz:
  Inferring Informational Goals from Free-Text Queries: A Bayesian Approach. 230-237
  
• Holger H. Hoos, Thomas Stützle:
  Evaluating Las Vegas Algorithms: Pitfalls and Remedies. 238-245
  
• Michael C. Horsch, David L. Poole:
  An Anytime Algorithm for Decision Making under Uncertainty. 246-255
  
• Eric Horvitz, Jack S. Breese, David Heckerman, David Hovel, Koos Rommelse:
  The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. 256-265
  
• Pablo H. Ibargüengoytia, Luis Enrique Sucar, Sunil Vadera:
  Any Time Probabilistic Reasoning for Sensor Validation. 266-273
  
• Manfred Jaeger:
  Measure Selection: Notions of Rationality and Representation Independence. 274-281
  
• Jean-Yves Jaffray:
  Implementing Resolute Choice Under Uncertainty. 282-288
  
• Iman Jarkass, Michèle Rombaut:
  Dealing with uncertainty on the initial state of a Petri net. 289-295
  
• Wenxin Jiang, Martin A. Tanner:
  Hierarchical Mixtures-of-Experts for Exponential Family Regression Models with Generalized Linear Mean Functions: A Survey of Approximation and Consistency Results. 296-303
  
• Michael J. Kearns, Yishay Mansour:
  Exact Inference of Hidden Structure from Sample Data in noisy-OR Networks. 304-310
  
• Michael J. Kearns, Lawrence K. Saul:
  Large Deviation Methods for Approximate Probabilistic Inference. 311-319
  
• Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan:
  Mixture Representations for Inference and Learning in Boltzmann Machines. 320-327
  
• Vasilica Lepar, Prakash P. Shenoy:
  A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer Architectures for Computing Marginals of Probability Distributions. 328-337
  
• Chao-Lin Liu, Michael P. Wellman:
  Incremental Tradeoff Resolution in Qualitative Probabilistic Networks. 338-345
  
• Chao-Lin Liu, Michael P. Wellman:
  Using Qualitative Relationships for Bounding Probability Distributions. 346-353
  
• Thomas Lukasiewicz:
  Magic Inference Rules for Probabilistic Deduction under Taxonomic Knowledge. 354-361
  
• Anders L. Madsen, Finn Verner Jensen:
  Lazy Propagation in Junction Trees. 362-369
  
• Suzanne M. Mahoney, Kathryn B. Laskey:
  Constructing Situation Specific Belief Networks. 370-37
  
• Charles F. Manski:
  Treatment Choice in Heterogeneous Populations Using Experiments without Covariate Data. 379-385
  
• Marina Meila, David Heckerman:
  An Experimental Comparison of Several Clustering and Initialization Methods. 386-395
  
• Paul-André Monney:
  From Likelihood to Plausibility. 396-403
  
• Stefano Monti, Gregory F. Cooper:
  A Multivariate Discretization Method for Learning Bayesian Networks from Mixed Data. 404-413
  
• Benson Hin Kwong Ng, Kam-Fai Wong, Boon Toh Low:
  Resolving Conflicting Arguments under Uncertainties. 414-421
  
• Ronald Parr:
  Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems. 422-430
  
• David M. Pennock:
  Logarithmic Time Parallel Bayesian Inference. 431-438
  
• Mark A. Peot, Ross D. Shachter:
  Learning From What You Don't Observe. 439-446
  
• David Poole:
  Context-specific approximation in probabilistic inference. 447-454
  
• Irina Rish, Kalev Kask, Rina Dechter:
  Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding. 455-463
  
• Paola Sebastiani, Marco Ramoni:
  Decision Theoretic Foundations of Graphical Model Selection. 464-471
  
• Raffaella Settimi, Jim Q. Smith:
  On the Geometry of Bayesian Graphical Models with Hidden Variables. 472-479
  
• Ross D. Shachter:
  Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams). 480-487
  
• Yoram Singer:
  Switching Portfolios. 488-495
  
• Milan Studený:
  Bayesian Networks from the Point of View of Chain Graphs. 496-503
  
• Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman:
  Learning Mixtures of DAG Models. 504-513
  
• Nevin Lianwen Zhang:
  Probabilistic Inference in Influence Diagrams. 514-522
  
• Nevin Lianwen Zhang, Stephen S. Lee:
  Planning with Partially Observable Markov Decision Processes: Advances in Exact Solution Method. 523-530
  
• Andrea Bobbio:
  Flexible and Approximate Computation through State-Space Reduction. 531-538
  

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