15. UAI 1999: Stockholm, Sweden
Kathryn B. Laskey, Henri Prade (Eds.): 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, Lluis Godo, Sandra 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
- 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
- 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
- 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 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
- 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
- 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
- Ryszard Kowalczyk: On Quantified Linguistic Approximation. 351-358
- 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
- David A. McAllester, Satinder P. 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 Mislevy, Russell 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
- Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huang: Variational Learning in Mixed-State Dynamic Graphical Models. 522-530
- 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
- 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
- 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
- Yanping Xiang, Finn Verner Jensen: Inference in Multiply Sectioned Bayesian Networks with Extended Shafer-Shenoy and Lazy Propagation. 680-687
- Nevin Lianwen Zhang, Stephen S. Lee, Weihong Zhang: A Method for Speeding Up Value Iteration in Partially Observable Markov Decision Processes. 696-703
data released under the ODC-BY 1.0 license. See also our legal information page
last updated on 2013-06-18T214307 by the dblp team