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Gregory F. Cooper, Serafín Moral (Eds.): UAI '98: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, University of Wisconsin Business School, Madison, Wisconsin, USA, July 24-26, 1998. Morgan Kaufmann 1998 ISBN 1-55860-555-X- 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
- 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
- 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
- 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
- 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
- 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
- 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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