16. UAI 2000: Stanford, California, USA

• Teresa Alsinet, Lluis Godo:
  A Complete Calcultis for Possibilistic Logic Programming with Fuzzy Propositional Variables. 1-10
  
• Christophe Andrieu, Nando de Freitas, Arnaud Doucet:
  Reversible Jump MCMC Simulated Annealing for Neural Networks. 11-18
  
• Ann Becker, Dan Geiger, Christopher Meek:
  Perfect Tree-like Markovian Distributions. 19-23
  
• Daniel S. Bernstein, Shlomo Zilberstein, Neil Immerman:
  The Complexity of Decentralized Control of Markov Decision Processes. 32-37
  
• Jeff Bilmes:
  Dynamic Bayesian Multinets. 38-45
  
• Christopher M. Bishop, Michael E. Tipping:
  Variational Relevance Vector Machines. 46-53
  
• Craig Boutilier:
  Approximately Optimal Monitoring of Plan Preconditions. 54-62
  
• Urszula Chajewska, Daphne Koller:
  Utilities as Random Variables: Density Estimation and Structure Discovery. 63-71
  
• Jian Cheng, Marek J. Druzdzel:
  Computational Investigation of Low-Discrepancy Sequences in Simulation Algorithms for Bayesian Networks. 72-81
  
• David Maxwell Chickering, David Heckerman:
  A Decision Theoretic Approach to Targeted Advertising. 82-88
  
• Frans Coetzee, Steve Lawrence, C. Lee Giles:
  Bayesian Classification and Feature Selection from Finite Data Sets. 89-97
  
• Gregory F. Cooper:
  A Bayesian Method for Causal Modeling and Discovery Under Selection. 98-106
  
• Fabio Gagliardi Cozman:
  Separation Properties of Sets of Probability Measures. 107-114
  
• James Cussens:
  Stochastic Logic Programs: Sampling, Inference and Applications. 115-122
  
• Adnan Darwiche:
  A Differential Approach to Inference in Bayesian Networks. 123-132
  
• Adnan Darwiche:
  Any-Space Probabilistic Inference. 133-142
  
• Sanjoy Dasgupta:
  Experiments with Random Projection. 143-151
  
• Sanjoy Dasgupta, Leonard J. Schulman:
  A Two-Round Variant of EM for Gaussian Mixtures. 152-159
  
• Ian Davidson:
  Minimum Message Length Clustering Using Gibbs Sampling. 160-167
  
• Scott Davies, Andrew W. Moore:
  Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks with Mixed Continuous And Discrete Variables. 168-175
  
• Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, Stuart J. Russell:
  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. 176-183
  
• Brendan J. Frey, Nebojsa Jojic:
  Learning Graphical Models of Images, Videos and Their Spatial Transformations. 184-191
  
• Nir Friedman, Dan Geiger, Noam Lotner:
  Likelihood Computations Using Value Abstraction. 192-200
  
• Nir Friedman, Daphne Koller:
  Being Bayesian about Network Structure. 201-210
  
• Nir Friedman, Iftach Nachman:
  Gaussian Process Networks. 211-219
  
• Phan Hong Giang, Prakash P. Shenoy:
  A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic Beliefs. 220-229
  
• Peter Gorniak, David Poole:
  Building a Stochastic Dynamic Model of Application Use. 230-237
  
• Peter Grünwald:
  Maximum Entropy and the Glasses You are Looking Through. 238-246
  
• Joseph Y. Halpern:
  Conditional Plausibility Measures and Bayesian Networks. 247-255
  
• Michael Harvey, Radford M. Neal:
  Inference for Belief Networks Using Coupling From the Past. 256-263
  
• David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie:
  Dependency Networks for Collaborative Filtering and Data Visualization. 264-273
  
• Søren Højsgaard:
  YGGDRASIL-A statistical package for learning Split Models. 274-281
  
• Michael C. Horsch, William S. Havens:
  Probabilistic Arc Consistency: A Connection between Constraint Reasoning and Probabilistic Reasoning. 282-290
  
• Tony Jebara, Tommi Jaakkola:
  Feature Selection and Dualities in Maximum Entropy Discrimination. 291-300
  
• Radim Jirousek:
  Marginalization in Composed Probabilistic Models. 301-308
  
• Souhila Kaci, Salem Benferhat, Didier Dubois, Henri Prade:
  A principled analysis of merging operations in possibilistic logic. 24-31
  
• Michael J. Kearns, Yishay Mansour, Satinder P. Singh:
  Fast Planning in Stochastic Games. 309-316
  
• Uffe Kjærulff, Linda C. van der Gaag:
  Making Sensitivity Analysis Computationally Efficient. 317-325
  
• Daphne Koller, Ronald Parr:
  Policy Iteration for Factored MDPs. 326-334
  
• Pierfrancesco La Mura:
  Game Networks. 335-342
  
• Pedro Larrañaga, Ramon Etxeberria, José Antonio Lozano, José Manuel Peña:
  Combinatonal Optimization by Learning and Simulation of Bayesian Networks. 343-352
  
• Tsai-Ching Lu, Marek J. Druzdzel, Tze-Yun Leong:
  Causal Mechanism-based Model Constructions. 353-362
  
• Thomas Lukasiewicz:
  Credal Networks under Maximum Entropy. 363-370
  
• Peter McBurney, Simon Parsons:
  Risk Agoras: Dialectical Argumentation for Scientific Reasoning. 371-379
  
• Marina Meila, Tommi Jaakkola:
  Tractable Bayesian Learning of Tree Belief Networks. 380-388
  
• Brian Milch, Daphne Koller:
  Probabilistic Models for Agent's Beliefs and Decisions. 389-396
  
• Andrew W. Moore:
  The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data. 397-405
  
• Andrew Y. Ng, Michael I. Jordan:
  PEGASUS: A policy search method for large MDPs and POMDPs. 406-415
  
• Thomas D. Nielsen, Finn Verner Jensen:
  Representing and Solving Asymmetric Bayesian Decision Problems. 416-425
  
• Thomas D. Nielsen, Pierre-Henri Wuillemin, Finn Verner Jensen, Uffe Kjærulff:
  Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example. 426-435
  
• Dennis Nilsson, Steffen L. Lauritzen:
  Evaluating Influence Diagrams using LIMIDs. 436-445
  
• Luis E. Ortiz, Leslie Pack Kaelbling:
  Adaptive Importance Sampling for Estimation in Structured Domains. 446-454
  
• Tim Paek, Eric Horvitz:
  Conversation as Action Under Uncertainty. 455-464
  
• Dmitry Pavlov, Heikki Mannila, Padhraic Smyth:
  Probabilistic Models for Query Approximation with Large Sparse Binary Data Sets. 465-472
  
• David M. Pennock, Eric Horvitz, Steve Lawrence, C. Lee Giles:
  Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach. 473-480
  
• David M. Pennock, Michael P. Wellman:
  Compact Securities Markets for Pareto Optimal Reallocation of Risk. 481-488
  
• Leonid Peshkin, Kee-Eung Kim, Nicolas Meuleau, Leslie Pack Kaelbling:
  Learning to Cooperate via Policy Search. 489-496
  
• Pascal Poupart, Craig Boutilier:
  Value-Directed Belief State Approximation for POMDPs. 497-506
  
• David V. Pynadath, Michael P. Wellman:
  Probabilistic State-Dependent Grammars for Plan Recognition. 507-514
  
• Silja Renooij, Linda C. van der Gaag, Simon Parsons, Shaw Green:
  Pivotal Pruning of Trade-offs in QPNs. 515-522
  
• Dale Schuurmans, Finnegan Southey:
  Monte Carlo inference via greedy importance sampling. 523-532
  
• Marc Sebban, Richard Nock:
  Combining Feature and Example Pruning by Uncertainty Minimization. 533-540
  
• Satinder P. Singh, Michael J. Kearns, Yishay Mansour:
  Nash Convergence of Gradient Dynamics in General-Sum Games. 541-548
  
• Claus Skaanning:
  A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters. 549-557
  
• Harald Steck:
  On the Use of Skeletons when Learning in Bayesian Networks. 558-565
  
• Amos J. Storkey:
  Dynamic Trees: A Structured Variational Method Giving Efficient Propagation Rules. 566-573
  
• Loo-Nin Teow, Kia-Fock Loe:
  An Uncertainty Framework for Classification. 574-579
  
• Jin Tian:
  A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks. 580-588
  
• Jin Tian, Judea Pearl:
  Probabilities of Causation: Bounds and Identification. 589-598
  
• Shivakumar Vaithyanathan, Byron Dom:
  Model-Based Hierarchical Clustering. 599-608
  
• Jirina Vejnarová:
  Conditional Independence and Markov Properties in Possibility Theory. 609-616
  
• Haiqin Wang, Marek J. Druzdzel:
  User Interface Tools for Navigation in Conditional Probability Tables and Elicitation of Probabilities in Bayesian Networks. 617-625
  
• Wim Wiegerinck:
  Variational Approximations between Mean Field Theory and the Junction Tree Algorithm. 626-633
  
• David M. Williamson, Russell Almond, Robert Mislevy:
  Model Criticism of Bayesian Networks with Latent Variables. 634-643
  
• Frank Wittig, Anthony Jameson:
  Exploiting Qualitative Knowledge in the Learning of Conditional Probabilities of Bayesian Networks. 644-652
  

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