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ORCID14th UAI 1998: Madison, Wisconsin, USA
- Gregory F. Cooper, Serafín Moral:
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 - 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 - Fábio 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 Russell:
Learning the Structure of Dynamic Probabilistic Networks. 139-147 - Alexander Gammerman, Volodya Vovk, 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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