11. UAI 1995: Montreal, Quebec, Canada

@proceedings{DBLP:conf/uai/1995,
  editor    = {Philippe Besnard and
               Steve Hanks},
  title     = {UAI '95: Proceedings of the Eleventh Annual Conference on Uncertainty
               in Artificial Intelligence, August 18-20, 1995, Montreal, Quebec,
               Canada},
  booktitle = {UAI},
  publisher = {Morgan Kaufmann},
  year      = {1995},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

• Fahiem Bacchus, Adam J. Grove:
  Graphical models for preference and utility. 3-10
  
• Alexander Balke, Judea Pearl:
  Counterfactuals and Policy Analysis in Structural Models. 11-18
  
• Salem Benferhat, Alessandro Saffiotti, Philippe Smets:
  Belief functions and default reasoning. 19-26
  
• Luca Boldrin, Claudio Sossai:
  An Algebraic Semantics for Possibilistic Logic. 27-35
  
• John S. Breese, Russ Blake:
  Automating Computer Bottleneck Detection with Belief Nets. 36-45
  
• Wray L. Buntine:
  Chain graphs for learning. 46-54
  
• Enrique Castillo, Remco R. Bouckaert, José María Sarabia, Cristina Solares:
  Error Estimation in Approximate Bayesian Belief Network Inference. 55-62
  
• Juan Luis Castro, Jose Manuel Zurita:
  Generating the Structure of a Fuzzy Rule under Uncertainty. 63-67
  
• Didier Cayrac, Didier Dubois, Henri Prade:
  Practical model-based diagnosis with qualitative possibilistic uncertainty. 68-76
  
• Tom Chávez, Ross D. Shachter:
  Decision Flexibility. 77-86
  
• David Maxwell Chickering:
  A Transformational Characterization of Equivalent Bayesian Network Structures. 87-98
  
• Adnan Darwiche:
  Conditioning Algorithms for Exact and Approximate Inference in Causal Networks. 99-107
  
• Luis M. de Campos, Serafín Moral:
  Independence Concepts for Convex Sets of Probabilities. 108-115
  
• Arthur L. Delcher, Adam J. Grove, Simon Kasif, Judea Pearl:
  Logarithmic-Time Updates and Queries in Probabilistic Networks. 116-124
  
• Denise Draper:
  Clustering Without (Thinking About) Triangulation. 125-133
  
• Eric Driver, Darryl Morrell:
  Implementation of Continuous Bayesian Networks Using Sums of Weighted Gaussians. 134-140
  
• Marek J. Druzdzel, Linda C. van der Gaag:
  Elicitation of Probabilities for Belief Networks: Combining Qualitative and Quantitative Information. 141-148
  
• Didier Dubois, Henri Prade:
  Numerical representations of acceptance. 149-156
  
• Kazuo J. Ezawa, Til Schuermann:
  Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures. 157-166
  
• Hélène Fargier, Jérôme Lang, Roger Martin-Clouaire, Thomas Schiex:
  A constraint satisfaction framework for decision under uncertainty. 167-174
  
• Nir Friedman, Joseph Y. Halpern:
  Plausibility Measures: A User's Guide. 175-184
  
• David Galles, Judea Pearl:
  Testing Identifiability of Causal Effects. 185-195
  
• Dan Geiger, David Heckerman:
  A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks. 196-207
  
• Moisés Goldszmidt:
  Fast Belief Update Using Order-of-Magnitude Probabilities. 208-216
  
• Benjamin N. Grosof:
  Transforming Prioritized Defaults and Specificity into Parallel Defaults. 217-228
  
• Peter Haddawy, AnHai Doan, Richard Goodwin:
  Efficient Decision-Theoretic Planning: Techniques and Empirical Analysis. 229-236
  
• Petr Hájek, Lluis Godo, Francesc Esteva:
  Fuzzy logic and probability. 237-244
  
• Steve Hanks, David Madigan, Jonathan Gavrin:
  Probabilistic Temporal Reasoning with Endogenous Change. 245-254
  
• David Harmanec:
  Toward a Characterization of Uncertainty Measure for the Dempster-Shafer Theory. 255-261
  
• David Heckerman, Ross D. Shachter:
  A Definition and Graphical Representation for Causality. 262-273
  
• David Heckerman, Dan Geiger:
  Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains. 274-284
  
• David Heckerman:
  A Bayesian Approach to Learning Causal Networks. 285-295
  
• Eric Horvitz, Matthew Barry:
  Display of Information for Time-Critical Decision Making. 296-305
  
• Eric Horvitz, Adrian Klein:
  Reasoning, Metareasoning, and Mathematical Truth: Studies of Theorem Proving under Limited Resources. 306-314
  
• Mark Hulme:
  Improved Sampling for Diagnostic Reasoning in Bayesian Networks. 315-322
  
• Finn Verner Jensen:
  Cautious Propagation in Bayesian Networks. 323-328
  
• Ali Jenzarli:
  Information/Relevance Influence Diagrams. 329-337
  
• George H. John, Pat Langley:
  Estimating Continuous Distributions in Bayesian Classifiers. 338-345
  
• Keiji Kanazawa, Daphne Koller, Stuart J. Russell:
  Stochastic simulation algorithms for dynamic probabilistic networks. 346-351
  
• Grigoris J. Karakoulas:
  Probabilistic Exploration in Planning while Learning. 352-361
  
• Young-Gyun Kim, Marco Valtorta:
  On the Detection of Conflicts in Diagnostic Bayesian Networks Using Abstraction. 362-367
  
• Uffe Kjærulff:
  HUGS: Combining Exact Inference and Gibbs Sampling in junction Trees. 368-375
  
• Alexander V. Kozlov, Jaswinder Pal Singh:
  Sensitivities: An Alternative to Conditional Probabilities for Bayesian Belief Networks. 376-385
  
• Paul J. Krause, John Fox, Philip N. Judson:
  Is There a Role for Qualitative Risk Assessment? 386-393
  
• Michael L. Littman, Thomas Dean, Leslie Pack Kaelbling:
  On the Complexity of Solving Markov Decision Problems. 394-402
  
• Christopher Meek:
  Causal inference and causal explanation with background knowledge. 403-410
  
• Christopher Meek:
  Strong completeness and faithfulness in Bayesian networks. 411-418
  
• Liem Ngo, Peter Haddawy, James Helwig:
  A Theoretical Framework for Context-Sensitive Temporal Probability Model Construction with Application to Plan Projection. 419-426
  
• Simon Parsons:
  Refining reasoning in qualitative probabilistic networks. 427-434
  
• Judea Pearl:
  On the Testability of Causal Models With Latent and Instrumental Variables. 435-443
  
• Judea Pearl, James M. Robins:
  Probabilistic evaluation of sequential plans from causal models with hidden variables. 444-453
  
• David Poole:
  Exploiting the Rule Structure for Decision Making within the Independent Choice Logic. 454-463
  
• Gregory M. Provan:
  Abstraction in Belief Networks: The Role of Intermediate States in Diagnostic Reasoning. 464-471
  
• David V. Pynadath, Michael P. Wellman:
  Accounting for Context in Plan Recognition, with Application to Traffic Monitoring. 472-481
  
• Prakash P. Shenoy:
  A New Pruning Method for Solving Decision Trees and Game Trees. 482-490
  
• Peter Spirtes:
  Directed Cyclic Graphical Representations of Feedback Models. 491-498
  
• Peter Spirtes, Christopher Meek, Thomas Richardson:
  Causal Inference in the Presence of Latent Variables and Selection Bias. 499-506
  
• Sampath Srinivas:
  Modeling failure priors and persistence in model-based diagnosis. 507-514
  
• Sampath Srinivas:
  A polynomial algorithm for computing the optimal repair strategy in a system with independent component failures. 515-522
  
• Sampath Srinivas, Eric Horvitz:
  Exploiting System Hierarchy to Compute Repair Plans in Probabilistic Model-Based Diagnosis. 523-531
  
• Michael P. Wellman, Matthew Ford, Kenneth Larson:
  Path Planning under Time-Dependent Uncertainty. 532-539
  
• Emil Weydert:
  Defaults and Infinitesimals Defeasible Inference by Nonarchimedean Entropy-Maximization. 540-547
  
• Nic Wilson:
  An Order of Magnitude Calculus. 548-555
  
• S. K. Michael Wong, Cory J. Butz, Yang Xiang:
  A Method for Implementing a Probabilistic Model as a Relational Database. 556-564
  
• Yang Xiang:
  Optimization of Inter-Subnet Belief Updating in Multiply Sectioned Bayesian Networks. 565-573
  
• Hong Xu, Philippe Smets:
  Generating Explanations for Evidential Reasoning. 574-581
  
• Nevin Lianwen Zhang:
  Inference with Causal Independence in the CPSC Network. 582-589