3. UAI 1987: Seattle, WA, USA
Laveen N. Kanal, Tod S. Levitt, John F. Lemmer (Eds.): UAI '87: Proceedings of the Third Annual Conference on Uncertainty in Artificial Intelligence, Seattle, WA, USA, July 10-12, 1987. Elsevier 1989 ISBN 0-444-87417-8- Gregory F. Cooper: An Algorithm for Computing Probabilistic Propositions. 3-14
- Henry E. Kyburg Jr.: Higher Order Probabilities. 15-22
- Mary McLeish: Nilsson's Probabilistic Entailment Extended to Dempster-Shafer Theory. 23-34
- Eric Neufeld, David L. Poole: Towards Solving the Multiple Extension Problem: Combining Defaults and Probability. 35-44
- Ben P. Wise: Satisfaction of Assumptions is a Weak Predictor of Performance. 45-54
- Ben P. Wise, Bruce M. Perrin, David S. Vaughan, Robert M. Yadrick: Evaluation of Uncertain Inference Models III: The Role of Tuning. 55-62
- John Yen: Can Evidence be Combined in the Dempster-Shafer Theory? 63-72
- Thomas O. Binford, Tod S. Levitt, Wallace B. Mann: Bayesian Inference in Model-Based Machine Vision. 73-96
- Lashon B. Booker, Naveen Hota, Gavin Hemphill: Computing Belief Commitments Using Tensor Products. 97-108
- Wray L. Buntine: Decision Tree Induction Systems: A Bayesian Analysis. 109-128
- Max Henrion: Some Practical Issues in Constructing Belief Networks. 161-174
- Ross D. Shachter, David M. Eddy, Vic Hasselblad, Robert Wolpert: A Heuristic Bayesian Approach to Knowledge Acquisition: Application to the Analysis of Tissue-Type Plasminogen Activator. 183-190
- Thomas B. Slack: Advantages and a Limitation of Using LEG Nets in a Real Time Problem. 191-198
- David J. Spiegelhalter: A Unified Approach to Imprecision and Sensitivity of Beliefs in Expert Systems. 199-208
- Gautam Biswas, Tejwansh S. Anand: Using the Dempster-Shafer Scheme in a Mixed-Initiative Expert System Shell. 223-240
- Piero P. Bonissone, Nancy C. Wood: T-Norm Based Reasoning in Situation Assessment Applications. 241-256
- Paul R. Cohen: Steps Towards Programs that Manage Uncertainty. 257-266
- Bruce D'Ambrosio: A Hybrid Approach to Reasoning Under Uncertainty. 267-284
- Eric Horvitz: Reasoning about Beliefs and Actions Under Computational Resource Constraints. 301-324
- John Yen: Implementing Evidential Reasoning in Expert Systems. 333-346
- Rich Caruana: The Automatic Training of Rule Bases that Use Numerical Uncertainty Representations. 347-356
- Norman C. Dalkey: The Inductive Logic of Information Systems. 375-386
- Chris Elsaesser: Explanation of Probabilistic Inference. 387-400
- Spencer Star: Theory-Based Inductive Learning: An Integration of Symbolic and Quantitative Methods. 401-424
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last updated on 2013-06-18T211851 by the dblp team