Next: About this document
Up: Variational Probabilistic Inference and
Previous: Appendix C. Convexity

D'Ambrosio, B. (1993). Incremental probabilistic inference.
In Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence. San Mateo, CA: Morgan Kaufmann.

D'Ambrosio, B. (1994). Symbolic probabilistic inference in large BN20
networks. In Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence. San Mateo, CA: Morgan Kaufmann.

Cooper, G. (1985). NESTOR: A computerbased medical diagnostic
aid that integrates causal and probabilistic knowledge.
Ph.D. Dissertation, Medical Informatics Sciences, Stanford
University, Stanford, CA. (Available from UMI at
http://wwwlib.umi.com/dissertations/main).

Cooper, G. (1990). The computational complexity of probabilistic
inference using Bayesian belief networks. Artificial
Intelligence, 42, 393405.

Dagum, P., & Horvitz, E. (1992). Reformulating inference problems
through selective conditioning. In Proceedings of the Eighth
Annual Conference on Uncertainty in Artificial Intelligence.

Dagum, P., & Horvitz, E. (1993). A Bayesian analysis of simulation
algorithms for inference in Belief networks. Networks, 23,
499516.

Dagum, P., & Luby, M. (1993). Approximate probabilistic reasoning in
Bayesian belief networks is NPhard. Artificial Intelligence,
60, 141153.

Dechter, R. (1997). Minibuckets: A general scheme of generating
approximations in automated reasoning. In Proceedings of
the Fifteenth International Joint Conference on Artificial Intelligence.

Dechter, R. (1998). Bucket elimination: A unifying framework for probabilistic
inference. In M. I. Jordan (Ed.), Learning in Graphical Models.
Cambridge, MA: MIT Press.

Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from
incomplete data via the EM algorithm. Journal of the Royal
Statistical Society B, 39, 138.

Draper, D., & Hanks, S. (1994). Localized partial evaluation of belief
networks. In Proceedings of the Tenth Annual Conference on
Uncertainty in Artificial Intelligence.

Fung, R., & Chang, K. C. (1990). Weighting and integrating
evidence for stochastic simulation in Bayesian networks. In
Proceedings of Fifth Conference on Uncertainty in Artificial
Intelligence. Amsterdam: Elsevier Science.

Gelfand, A., & Smith, A. (1990). Samplingbased approaches to
calculating marginal Densities. Journal of the American Statistical
Association, 85, 398409.

Heckerman, D. (1989). A tractable inference algorithm for
diagnosing multiple diseases. In Proceedings of the Fifth
Conference on Uncertainty in Artificial Intelligence.

Henrion, M. (1991). Searchbased methods to bound diagnostic
probabilities in very large belief nets. In Proceedings of Seventh
Conference on Uncertainty in Artificial Intelligence.

Horvitz, E. Suermondt, H., & Cooper, G. (1989). Bounded conditioning:
Flexible inference for decisions under scarce resources. In
Proceedings of Fifth Conference on Uncertainty in Artificial
Intelligence.

Jaakkola, T. (1997). Variational methods for inference
and learning in graphical models. PhD thesis, Department of
Brain and Cognitive Sciences, Massachusetts Institute of Technology.

Jaakkola, T., & Jordan, M. (1996). Recursive algorithms for
approximating probabilities in graphical models. In Advances of
Neural Information Processing Systems 9. Cambridge, MA:
MIT Press.

Jensen, C. S., Kong, A., & Kjærulff, U. (1995).
BlockingGibbs sampling in very large probabilistic expert
systems. International Journal of HumanComputer
Studies, 42, 647666.

Jensen, F. (1996). Introduction to Bayesian networks.
New York: Springer.

Jordan, M., Ghaharamani, Z. Jaakkola, T., & Saul, L. (in press).
An introduction to variational methods for graphical models.
Machine Learning.

Lauritzen, S., & Spiegelhalter, D. (1988).
Local computations with probabilities on graphical
structures and their application to expert systems
(with discussion). Journal of the Royal Statistical
Society B, 50, 157224.

MacKay, D. J. C. (1998). Introduction to Monte Carlo methods.
In M. I. Jordan (Ed.), Learning in Graphical Models.
Cambridge, MA: MIT Press.

Middleton, B., Shwe, M., Heckerman, D., Henrion, M., Horvitz, E., Lehmann, H.,
& Cooper, G. (1990). Probabilistic diagnosis using a reformulation of
the INTERNIST1/QMR knowledge base II. Evaluation of diagnostic
performance. Section on Medical Informatics Technical report
SMI900329, Stanford University.

Miller, R. A., Fasarie, F. E., & Myers, J. D. (1986).
Quick medical reference (QMR) for diagnostic assistance.
Medical Computing, 3, 3448.

Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems.
San Mateo, CA: Morgan Kaufmann.

Peng, Y., & Reggia, J. (1987). A probabilistic causal model
for diagnostic problem solving  Part 2: Diagnostic strategy.
IEEE Trans. on Systems, Man, and Cybernetics: Special Issue
for Diagnosis, 17, 395406.

Poole, D. (1997). Probabilistic partial evaluation: Exploiting rule structure
in probabilistic inference. In Proceedings of the Fifteenth
International Joint Conference on Artificial Intelligence.

Rockafellar, R. (1972). Convex Analysis. Princeton University Press.

Shachter, R. D., & Peot, M. (1990). Simulation approaches to
general probabilistic inference on belief networks. In
Proceedings of Fifth Conference on Uncertainty in Artificial
Intelligence. Elsevier Science: Amsterdam.

Shenoy, P. P. (1992). Valuationbased systems for Bayesian
decision analysis. Operations Research, 40, 463484.

Shwe, M., & Cooper, G. (1991). An empirical analysis of
likelihood  weighting simulation on a large, multiply connected
medical belief network. Computers and Biomedical Research,
24, 453475.

Shwe, M., Middleton, B., Heckerman, D., Henrion, M., Horvitz, E., Lehmann, H.,
& G. Cooper (1991). Probabilistic diagnosis using a reformulation of
the INTERNIST1/QMR knowledge base I. The probabilistic model and
inference algorithms. Methods of Information in Medicine,
30, 241255.
Next: About this document
Up: Variational Probabilistic Inference and
Previous: Appendix C. Convexity
Michael Jordan
Sun May 9 16:22:01 PDT 1999