Course Reader for STA 294: Belief Networks

Instructor: Mark Alan Peot, 919-660-6558

 

Contents

  1. Ross Shachter, Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann, 1998.
  2. Mark Peot and Ross Shachter, Fusion and propagation with multiple observations in belief networks. Artificial Intelligence, 48 (1991): 299-318.
  3. Ann Becker and Dan Geiger, A sufficiently fast algorithm for finding close to optimal junction trees. Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann, 1996.
  4. Gregory Cooper, The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks. Artificial Intelligence, 42 (1990): 393-405.
  5. Max Henrion, Malcolm Pradhan, Brendan Del Favero, Kurt Huang, Gregory Provan, and Paul O’Rorke, Why is diagnosis using belief networks insensitive to imprecision in probabilities? Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann, 1996.
  6. David Heckerman and John Breese, A New Look at Causal Independence. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann, 1994.
  7. David Heckerman, Eric Horvitz, and Bharat Nathwani, Toward Normative Expert Systems: Part I The Pathfinder Project. Methods of Information in Medicine, 31(1992): 90-105.
  8. David Heckerman and Bharat Nathwani, Toward Normative Expert Systems: Part II Probability-Based Representations for Efficient Knowledge Acquisition and Inference. Methods of Information in Medicine, 31(1992): 106-16.
  9. David Heckerman and Eric Horvitz, Inferring Informational Goals from Free-Text Queries: A Bayesian Approach. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann, 1998.
  10. Eric Horvitz, Jack Breese, David Heckerman, David Hovel, and Koos Rommelse. The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann, 1998.
  11. Malcolm Pradhan and Paul Dagum, Optimal Monte Carlo Estimation of Belief Network Inference. Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann, 1996.
  12. Alexander Kozlov and Daphne Koller, Nonuniform dynamic discretization in hybrid networks. Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann, 1997.
  13. Paul Dagum and Michael Luby, Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artificial Intelligence, 60(1993): 141-53.
  14. David Heckerman, A Tutorial on Learning With Bayesian Networks. MSR-TR-95-06, Microsoft Research, Advanced Technology Division, Microsoft Corporation, Redmond, WA.
  15. Peter Cheeseman and John Stutz, Bayesian Classification (Autoclass): Theory and Results. Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann, 1997.

 

Copyright 1999, Mark Alan Peot