15. UAI 1999: Stockholm, Sweden

• 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
  
• 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 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 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 P. Singh:
  On the Complexity of Policy Iteration. 401-408
  
• 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
  
• 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, B. M. P. Aleman, B. 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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