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15th UAI 1999: Stockholm, Sweden
- Kathryn B. Laskey, Henri Prade:
UAI '99: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, July 30 - August 1, 1999. Morgan Kaufmann 1999, ISBN 1-55860-614-9 - Teresa Alsinet, Lluís Godo, Sandra A. Sandri:
On the Semantics and Automated Deduction for PLFC, a Logic of Possibilistic Uncertainty and Fuzziness. 3-12 - Gustavo Arroyo-Figueroa, Luis Enrique Sucar:
A Temporal Bayesian Network for Diagnosis and Prediction. 13-20 - Hagai Attias:
Inferring Parameters and Structure of Latent Variable Models by Variational Bayes. 21-30 - Katy S. Azoury, Manfred K. Warmuth:
Relative Loss Bounds for On-line Density Estirnation with the Exponential Family of Distributions. 31-40 - Philip S. Barry, Kathryn B. Laskey:
An Application of Uncertain Reasoning to Requirements Engineering. 41-48 - Ann Becker, Reuven Bar-Yehuda, Dan Geiger:
Random Algorithms for the Loop Cutset Problem. 49-56 - Salem Benferhat, Didier Dubois, Laurent Garcia, Henri Prade:
Possibilistic logic bases and possibilistic graphs. 57-64 - Magnus Boman, Paul Davidsson, Håkan L. S. Younes:
Artificial Decision Making Under Uncertainty in Intelligent Buildings. 65-70 - Craig Boutilier, Ronen I. Brafman, Holger H. Hoos, David Poole:
Reasoning With Conditional Ceteris Paribus Preference Statements. 71-80 - Craig Boutilier, Moisés Goldszmidt, Bikash Sabata:
Continuous Value Function Approximation for Sequential Bidding Policies. 81-90 - Xavier Boyen, Nir Friedman, Daphne Koller:
Discovering the Hidden Structure of Complex Dynamic Systems. 91-100 - Jie Cheng, Russell Greiner:
Comparing Bayesian Network Classifiers. 101-108 - David Maxwell Chickering, David Heckerman:
Fast Learning from Sparse Data. 109-115 - Gregory F. Cooper, Changwon Yoo:
Causal Discovery from a Mixture of Experimental and Observational Data. 116-125 - James Cussens:
Loglinear models for first-order probabilistic reasoning. 126-133 - Sanjoy Dasgupta:
Learning Polytrees. 134-141 - Denver Dash, Marek J. Druzdzel:
A Hybrid Anytime Algorithm for the Construction of Causal Models From Sparse Data. 142-149 - Richard Dearden, Nir Friedman, David Andre:
Model based Bayesian Exploration. 150-159 - Michael I. Dekhtyar, Alex Dekhtyar, V. S. Subrahmanian:
Hybrid Probabilistic Programs: Algorithms and Complexity. 160-169 - Didier Dubois, Michel Grabisch, Henri Prade, Philippe Smets:
Assessing the value of a candidate: Comparing belief function and possibility theories. 170-177 - Kazuo J. Ezawa, Gregory Napiorkowski, Mariusz Kossarski:
Evaluation of Distributed Intelligence on the Smart Card. 178-187 - Hélène Fargier, Patrice Perny:
Qualitative Models for Decision Under Uncertainty without the Commensurability Assumption. 188-195 - Nir Friedman, Moisés Goldszmidt, Abraham J. Wyner:
Data Analysis with Bayesian Networks: A Bootstrap Approach. 196-205 - Nir Friedman, Iftach Nachman, Dana Pe'er:
Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm. 206-215 - Dan Geiger, David Heckerman:
Parameter Priors for Directed Acyclic Graphical Models and the Characteriration of Several Probability Distributions. 216-225 - Dan Geiger, Christopher Meek:
Quantifier Elimination for Statistical Problems. 226-235 - Phan Hong Giang, Prakash P. Shenoy:
On Transformations between Probability and Spolinian Disbelief Functions. 236-244 - Robert P. Goldman, Christopher W. Geib, Christopher A. Miller:
A New Model of Plan Recognition. 245-254 - Dominique Gruyer, Véronique Berge-Cherfaoui:
Multi-objects association in perception of dynamical situation. 255-262 - Vu A. Ha, Peter Haddawy:
A Hybrid Approach to Reasoning with Partially Elicited Preference Models. 263-270 - David Harmanec:
Faithful Approximations of Belief Functions. 271-278 - Jesse Hoey, Robert St-Aubin, Alan J. Hu, Craig Boutilier:
SPUDD: Stochastic Planning using Decision Diagrams. 279-288 - Thomas Hofmann:
Probabilistic Latent Semantic Analysis. 289-296 - Michael C. Horsch, David L. Poole:
Estimating the Value of Computation in Flexible Information Refinement. 297-304 - Eric Horvitz, Andy Jacobs, David Hovel:
Attention-Sensitive Alerting. 305-313 - Kalev Kask, Rina Dechter:
Mini-Bucket Heuristics for Improved Search. 314-323 - Daphne Koller, Uri Lerner, Dragomir Anguelov:
A General Algorithm for Approximate Inference and Its Application to Hybrid Bayes Nets. 324-333 - Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri:
On Supervised Selection of Bayesian Networks. 334-342 - Kevin B. Korb, Ann E. Nicholson, Nathalie Jitnah:
Bayesian Poker. 343-350 - Ryszard Kowalczyk:
On Quantified Linguistic Approximation. 351-358 - Henry E. Kyburg Jr., Choh Man Teng:
Choosing Among Interpretations of Probability. 359-365 - Pierfrancesco La Mura, Yoav Shoham:
Expected Utility Networks. 366-373 - Christopher Lusena, Tong Li, Shelia Sittinger, Chris Wells, Judy Goldsmith:
My Brain is Full: When More Memory Helps. 374-381 - Anders L. Madsen, Finn Verner Jensen:
Lazy Evaluation of Symmetric Bayesian Decision Problems. 382-390 - Suzanne M. Mahoney, Kathryn B. Laskey:
Representing and Combining Partially Specified CPTs. 391-400 - Yishay Mansour, Satinder Singh:
On the Complexity of Policy Iteration. 401-408 - David A. McAllester, Satinder Singh:
Approximate Planning for Factored POMDPs using Belief State Simplification. 409-416 - Nicolas Meuleau, Kee-Eung Kim, Leslie Pack Kaelbling, Anthony R. Cassandra:
Solving POMDPs by Searching the Space of Finite Policies. 417-426 - Nicolas Meuleau, Leonid Peshkin, Kee-Eung Kim, Leslie Pack Kaelbling:
Learning Finite-State Controllers for Partially Observable Environments. 427-436 - Robert J. Mislevy, Russell G. Almond, Duanli Yan, Linda S. Steinberg:
Bayes Nets in Educational Assessment: Where the Numbers Come From. 437-446 - Stefano Monti, Gregory F. Cooper:
A Bayesian Network Classifier that Combines a Finite Mixture Model and a NaIve Bayes Model. 447-456 - Kevin P. Murphy:
A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables. 457-466 - Kevin P. Murphy, Yair Weiss, Michael I. Jordan:
Loopy Belief Propagation for Approximate Inference: An Empirical Study. 467-475 - James W. Myers, Kathryn B. Laskey, Tod S. Levitt:
Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms. 476-485 - Julian R. Neil, Chris S. Wallace, Kevin B. Korb:
Learning Bayesian Networks with Restricted Causal Interactions. 486-493 - Hien Nguyen, Peter Haddawy:
The Decision-Theoretic Interactive Video Advisor. 494-501 - Thomas D. Nielsen, Finn Verner Jensen:
Welldefined Decision Scenarios. 502-511 - Luis E. Ortiz, Leslie Pack Kaelbling:
Accelerating EM: An Empirical Study. 512-521 - Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huang:
Variational Learning in Mixed-State Dynamic Graphical Models. 522-530 - David M. Pennock, Michael P. Wellman:
Graphical Representations of Consensus Belief. 531-540 - Avi Pfeffer, Daphne Koller, Brian Milch, Ken T. Takusagawa:
SPOOK: A system for probabilistic object-oriented knowledge representation. 541-550 - Luigi Portinale, Andrea Bobbio:
Bayesian Networks for Dependability Analysis: an Application to Digital Control Reliability. 551-558 - Silja Renooij, Linda C. van der Gaag:
Enhancing QPNs for Trade-off Resolution. 559-566 - Régis Sabbadin:
A Possibilistic Model for Qualitative Sequential Decision Problems under Uncertainty in Partially Observable Environments. 567-574 - David A. Schum:
Inference Networks and the Evaluation of Evidence: Alternative Analyses. 575-584 - Raffaella Settimi, Jim Q. Smith, A. S. Gargoum:
Approximate Learning in Complex Dynamic Bayesian Networks. 585-593 - Ross D. Shachter:
Efficient Value of Information Computation. 594-601 - Hagit Shatkay:
Learning Hidden Markov Models with Geometrical Constraints. 602-611 - Philippe Smets:
Practical Uses of Belief Functions. 612-621 - Masami Takikawa, Bruce D'Ambrosio:
Multiplicative Factorization of Noisy-Max. 622-630 - Leendert W. N. van der Torre, Yao-Hua Tan:
An Update Semantics for Defeasible Obligations. 631-638 - Volker Tresp, Michael Haft, Reimar Hofmann:
Mixture Approximations to Bayesian Networks. 639-646 - Linda C. van der Gaag, Silja Renooij, C. L. M. Witteman, Berthe M. P. Aleman, Babs G. Taal:
How to Elicit Many Probabilities. 647-654 - Frans Voorbraak:
Probabilistic Belief Change: Expansion, Conditioning and Constraining. 655-662 - Robert L. Welch, Clayton Smith:
Bayesian Control for Concentrating Mixed Nuclear Waste. 663-669 - S. K. Michael Wong, Cory J. Butz:
Contextual Weak Independence in Bayesian Networks. 670-679 - Yanping Xiang, Finn Verner Jensen:
Inference in Multiply Sectioned Bayesian Networks with Extended Shafer-Shenoy and Lazy Propagation. 680-687 - Yanping Xiang, Kim-Leng Poh:
Time-Critical Dynamic Decision Making. 688-695 - Nevin Lianwen Zhang, Stephen S. Lee, Weihong Zhang:
A Method for Speeding Up Value Iteration in Partially Observable Markov Decision Processes. 696-703
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