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ORCID13th UAI 1997: Providence, Rhode Island, USA
- Dan Geiger, Prakash P. Shenoy:
UAI '97: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, Brown University, Providence, Rhode Island, USA, August 1-3, 1997. Morgan Kaufmann 1997, ISBN 1-55860-485-5 - Eric Bauer, Daphne Koller, Yoram Singer:
Update Rules for Parameter Estimation in Bayesian Networks. 3-13 - Ami Berler, Solomon Eyal Shimony:
Bayes Networks for Sonar Sensor Fusion. 14-21 - John Bigham:
Exploiting Uncertain and Temporal Information in Correlation. 22-29 - Craig Boutilier:
Correlated Action Effects in Decision Theoretic Regression. 30-37 - Alex G. Büchner, Werner Dubitzky, Alfons Schuster, Philippe Lopes, Peter G. O'Donoghue, John G. Hughes, David A. Bell, Kenneth Adamson, John A. White, John M. C. C. Anderson, Maurice D. Mulvenna:
Corporate Evidential Decision Making in Performance Prediction Domains. 38-45 - Luis M. de Campos, Juan F. Huete:
Algorithms for Learning Decomposable Models and Chordal Graphs. 46-53 - Anthony R. Cassandra, Michael L. Littman, Nevin Lianwen Zhang:
Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes. 54-61 - Urszula Chajewska, Joseph Y. Halpern:
Defining Explanation in Probabilistic Systems. 62-71 - Adrian Y. W. Cheuk, Craig Boutilier:
Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks. 72-79 - David Maxwell Chickering, David Heckerman, Christopher Meek:
A Bayesian Approach to Learning Bayesian Networks with Local Structure. 80-89 - Tongsheng Chu, Yang Xiang:
Exploring Parallelism in Learning Belief Networks. 90-98 - Matthew S. Collins, Jonathan J. Oliver:
Efficient Induction of Finite State Automata. 99-107 - Fábio Gagliardi Cozman:
Robustness Analysis of Bayesian Networks with Local Convex Sets of Distributions. 108-115 - Adnan Darwiche, Gregory M. Provan:
A Standard Approach for Optimizing Belief Network Inference Using Query DAGs. 116-123 - Thomas L. Dean, Robert Givan, Sonia M. Leach:
Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes. 124-131 - Rina Dechter, Irina Rish:
A Scheme for Approximating Probabilistic Inference. 132-141 - Søren L. Dittmer, Finn Verner Jensen:
Myopic Value of Information in Influence Diagrams. 142-149 - Jens Doerpmund:
Limitations of Skeptical Default Reasoning. 150-156 - Didier Dubois, Hélène Fargier, Henri Prade:
Decision-Making under Ordinal Preferences and Comparative Uncertainty. 157-164 - Nir Friedman, Moisés Goldszmidt:
Sequential Update of Bayesian Network Structure. 165-174 - Nir Friedman, Stuart Russell:
Image Segmentation in Video Sequences: A Probabilistic Approach. 175-181 - Judy Goldsmith, Michael L. Littman, Martin Mundhenk:
The Complexity of Plan Existence and Evaluation in Probabilistic Domains. 182-189 - Carla P. Gomes, Bart Selman:
Algorithm Portfolio Design: Theory vs. Practice. 190-197 - Russell Greiner, Adam J. Grove, Dale Schuurmans:
Learning Bayesian Nets that Perform Well. 198-207 - Adam J. Grove, Joseph Y. Halpern:
Probability Update: Conditioning vs. Cross-Entropy. 208-214 - Vu A. Ha, Peter Haddawy:
Problem-Focused Incremental Elicitation of Multi-Attribute Utility Models. 215-222 - David Heckerman, Christopher Meek:
Models and Selection Criteria for Regression and Classification. 223-228 - Luis D. Hernández, Serafín Moral:
Inference with Idempotent Valuations. 229-237 - Eric Horvitz, Jed Lengyel:
Perception, Attention, and Resources: A Decision-Theoretic Approach to Graphics Rendering. 238-249 - Eric Horvitz, Adam Seiver:
Time-Critical Action: Representations and Application. 250-257 - Jun Hu, Yang Xiang:
Learning Belief Networks in Domains with Recursively Embedded Pseudo Independent Submodels. 258-265 - Manfred Jaeger:
Relational Bayesian Networks. 266-273 - Radim Jirousek:
Composition of Probability Measures on Finite Spaces. 274-281 - Michael J. Kearns, Yishay Mansour, Andrew Y. Ng:
An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. 282-293 - Uffe Kjærulff:
Nested Junction Trees. 294-301 - Daphne Koller, Avi Pfeffer:
Object-Oriented Bayesian Networks. 302-313 - Alexander V. Kozlov, Daphne Koller:
Nonuniform Dynamic Discretization in Hybrid Networks. 314-325 - Henry E. Kyburg Jr.:
Probabilistic Acceptance. 326-333 - Kathryn B. Laskey, Suzanne M. Mahoney:
Network Fragments: Representing Knowledge for Constructing Probabilistic Models. 334-341 - Yan Lin, Marek J. Druzdzel:
Computational Advantages of Relevance Reasoning in Bayesian Belief Networks. 342-350 - Maite López-Sánchez, Ramón López de Mántaras, Carles Sierra:
Incremental Map Generation by Low Cost Robots Based on Possibility/Necessity Grids. 351-357 - Todd Michael Mansell:
A Target Classification Decision Aid. 358-365 - Christopher Meek, David Heckerman:
Structure and Parameter Learning for Causal Independence and Causal Interaction Models. 366-375 - Paul-André Monney:
Support and Plausibility Degrees in Generalized Functional Models. 376-383 - Scott B. Morris, Doug Cork, Richard E. Neapolitan:
Cognitive Processing of Causal Knowledge. 384-391 - David M. Pennock, Michael P. Wellman:
Representing Aggregate Belief through the Competitive Equilibrium of a Securities Market. 392-400 - Marco Ramoni, Paola Sebastiani:
Learning Bayesian Networks from Incomplete Databases. 401-408 - James M. Robins, Larry A. Wasserman:
Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs. 409-420 - Solomon Eyal Shimony, Carmel Domshlak, Eugene Santos Jr.:
Cost-Sharing in Bayesian Knowledge Bases. 421-428 - Yoav Shoham:
Conditional Utility, Utility Independence, and Utility Networks. 429-436 - Choh Man Teng:
Sequential Thresholds: Context Sensitive Default Extensions. 437-444 - Moshe Tennenholtz:
On Stable Multi-Agent Behavior in Face of Uncertainty. 445-452 - Bo Thiesson:
Score and Information for Recursive Exponential Models with Incomplete Data. 453-463 - Ian E. Thomas, Ingrid Zukerman, Jonathan J. Oliver, David W. Albrecht, Bhavani Raskutti:
Lexical Access for Speech Understanding using Minimum Message Length Encoding. 464-471 - Nevin Lianwen Zhang, Wenju Liu:
Region-Based Approximations for Planning in Stochastic Domains. 472-480 - Nevin Lianwen Zhang, Li Yan:
Independence of Causal Influence and Clique Tree Propagation. 481-488 - Nevin Lianwen Zhang, Weihong Zhang:
Fast Value Iteration for Goal-Directed Markov Decision Processes. 489-494
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