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ORCID9th UAI 1993: Washington, DC, USA
- David Heckerman, E. H. Mamdani:
UAI '93: Proceedings of the Ninth Annual Conference on Uncertainty in Artificial Intelligence, The Catholic University of America, Providence, Washington, DC, USA, July 9-11, 1993. Morgan Kaufmann 1993, ISBN 1-55860-306-9 - Marek J. Druzdzel, Herbert A. Simon:
Causality in Bayesian Belief Networks. 3-11 - Judea Pearl:
From Conditional Oughts to Qualitative Decision Theory. 12-22 - Russ B. Altman:
A Probabilistic Algorithm for Calculating Structure: Borrowing from Simulated Annealing. 23-31 - Scott A. Musman, Liwu Chang:
A Study of Scaling Issues in Bayesian Belief Networks for Ship Classification. 32-39 - Gregory M. Provan:
Tradeoffs in Constructing and Evaluating Temporal Influence Diagrams. 40-47 - Harold P. Lehmann, Ross D. Shachter:
End-User Construction of Influence Diagrams for Bayesian Statistics. 48-54 - Steven M. LaValle, Seth Hutchinson:
On Considering Uncertainty and Alternatives in Low-Level Vision. 55-63 - Paul Dagum, Adam Galper:
Forecasting Sleep Apnea with Dynamic Network Models. 64-71 - Peter J. Regan:
Normative Engineering Risk Management Systems. 72-79 - David Heckerman, Michael Shwe:
Diagnosis of Multiple Faults: A Sensitivity Analysis. 80-90 - Paul Dagum, Adam Galper:
Additive Belief-Network Models. 91-98 - Francisco Javier Díez:
Parameter adjustment in Bayes networks. The generalized noisy OR-gate. 99-105 - Didier Dubois, Henri Prade:
A fuzzy relation-based extension of Reggia's relational model for diagnosis handling uncertain and incomplete information. 106-113 - Morten Elvang-Gøransson, Paul J. Krause, John Fox:
Dialectic reasoning with inconsistent information. 114-121 - David Heckerman:
Causal Independence for Knowledge Acquisition and Inference. 122-127 - Eric Horvitz, Adrian C. Klein:
Utility-Based Abstraction and Categorization. 128-135 - Kathryn B. Laskey:
Sensitivity Analysis for Probability Assessments in Bayesian Networks. 136-142 - John F. Lemmer:
Causal Modeling. 143-151 - Izhar Matzkevich, Bruce Abramson:
Some Complexity Considerations in the Combination of Belief Networks. 152-158 - Izhar Matzkevich, Bruce Abramson:
Deriving A Minimal itI-map of a Belief Network Relative to a Target Ordering of its Nodes. 159-165 - Kim-Leng Poh, Michael R. Fehling:
Probabilistic Conceptual Network: A Belief Representation Scheme for Utility-Based Categorization. 166-173 - Kim-Leng Poh, Eric Horvitz:
Reasoning about the Value of Decision-Model Refinement: Methods and Application. 174-182 - William B. Poland, Ross D. Shachter:
Mixtures of Gaussians and Minimum Relative Entropy Techniques for Modeling Continuous Uncertainties. 183-190 - Prakash P. Shenoy:
Valuation Networks and Conditional Independence. 191-199 - Solomon Eyal Shimony:
Relevant Explanations: Allowing Disjunctive Assignments. 200-207 - Sampath Srinivas:
A Generalization of the Noisy-Or Model. 208-218 - Fahiem Bacchus:
Using First-Order Probability Logic for the Construction of Bayesian Networks. 219-226 - Marie desJardins:
Representing and Reasoning With Probabilistic Knowledge: A Bayesian Approach. 227-234 - John W. Egar, Mark A. Musen:
Graph-Grammar Assistance for Automated Generation of Influence Diagrams. 235-242 - Wai Lam, Fahiem Bacchus:
Using Causal Information and Local Measures to Learn Bayesian Networks. 243-250 - Ron Musick:
Minimal Assumption Distribution Propagation in Belief Networks. 251-258 - Moninder Singh, Marco Valtorta:
An Algorithm for the Construction of Bayesian Network Structures from Data. 259-265 - Joe Suzuki:
A Construction of Bayesian Networks from Databases Based on an MDL Principle. 266-273 - Soe-Tsyr Yuan:
Knowledge-Based Decision Model Construction for the Hierarchical Diagnosis: A Preliminary Report. 274-284 - Lisa J. Burnell:
A Synthesis of Logical and Probabilistic Reasoning for Program Understanding and Debugging. 285-291 - Peter Che, Richard E. Neapolitan, James R. Kenevan, Martha W. Evens:
An Implementation of a Method for Computing the Uncertainty in Inferred Probabilities in Belief Networks. 292-300 - Bruce D'Ambrosio:
Incremental Probabilistic Inference. 301-308 - Thomas L. Dean, Leslie Pack Kaelbling, Jak Kirman, Ann E. Nicholson:
Deliberation Scheduling for Time-Critical Sequential Decision Making. 309-316 - Marek J. Druzdzel, Max Henrion:
Intercausal Reasoning with Uninstantiated Ancestor Nodes. 317-325 - Dan Geiger, David Heckerman:
Inference Algorithms for Similarity Networks. 326-334 - Paul E. Lehner, Azar Sadigh:
Two Procedures for Compiling Influence Diagrams. 335-341 - Zhaoyu Li, Bruce D'Ambrosio:
An efficient approach for finding the MPE in belief networks. 342-349 - Todd Michael Mansell:
A method for Planning Given Uncertain and Incomplete Information. 350-358 - David Poole:
The use of conflicts in searching Bayesian networks. 359-367 - Carlos Rojas-Guzmán, Mark A. Kramer:
GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks. 368-375 - Sumit Sarkar:
Using Tree-Decomposable Structures to Approximate Belief Networks. 376-382 - Ross D. Shachter, Pierre Ndilikilikesha:
Using Potential Influence Diagrams for Probabilistic Inference and Decision Making. 383-390 - Thomas Verma, Judea Pearl:
Deciding Morality of Graphs is NP-complete. 391-399 - Nevin Lianwen Zhang, Runping Qi, David L. Poole:
Incremental computation of the value of perfect information in stepwise-decomposable influence diagrams. 400-410 - Salem Benferhat, Didier Dubois, Henri Prade:
Argumentative inference in uncertain and inconsistent knowledge bases. 411-419 - Adnan Darwiche:
Argument Calculus and Networks. 420-427 - John Fox, Paul J. Krause, Morten Elvang-Gøransson:
Argumentation as a General Framework for Uncertain Reasoning. 428-434 - Simon Parsons, E. H. Mamdani:
On reasoning in networks with qualitative uncertainty. 435-442 - S. K. Michael Wong, Zhiwei Wang:
Qualitative Measures of Ambiguity. 443-452 - Robert F. Bordley:
A Bayesian Variant of Shafer's Commonalities For Modelling Unforeseen Events. 453-460 - Craig Boutilier:
The Probability of a Possibility: Adding Uncertainty to Default Rules. 461-468 - Dimiter Driankov, Jérôme Lang:
Possibilistic decreasing persistence. 469-476 - J. W. Guan, David A. Bell:
Discounting and Combination Operations in Evidential Reasoning. 477-484 - Jürg Kohlas, Paul-André Monney:
Probabilistic Assumption-Based Reasoning. 485-491 - Serafín Moral, Luis M. de Campos:
Partially Specified Belief Functions. 492-499 - Philippe Smets:
Jeffrey's rule of conditioning generalized to belief functions. 500-505 - Fengming Song, Ping Liang:
Inference with Possibilistic Evidence. 506-514 - Carl G. Wagner, Bruce Tonn:
Constructing Lower Probabilities. 515-518 - Pei Wang:
Belief Revision in Probability Theory. 519-526 - Nic Wilson:
The Assumptions Behind Dempster's Rule. 527-534 - Hong Xu, Yen-Teh Hsia, Philippe Smets:
A Belief-Function Based Decision Support System. 535-542
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