References

• 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 computer-based 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, 393-405.
• 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, 499-516.
• Dagum, P., & Luby, M. (1993). Approximate probabilistic reasoning in Bayesian belief networks is NP-hard. Artificial Intelligence, 60, 141-153.
• Dechter, R. (1997). Mini-buckets: 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, 1-38.
• 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). Sampling-based approaches to calculating marginal Densities. Journal of the American Statistical Association, 85, 398-409.
• 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). Search-based 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). Blocking-Gibbs sampling in very large probabilistic expert systems. International Journal of Human-Computer Studies, 42, 647-666.
• 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, 157-224.
• 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 INTERNIST-1/QMR knowledge base II. Evaluation of diagnostic performance. Section on Medical Informatics Technical report SMI-90-0329, Stanford University.
• Miller, R. A., Fasarie, F. E., & Myers, J. D. (1986). Quick medical reference (QMR) for diagnostic assistance. Medical Computing, 3, 34-48.
• 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, 395-406.
• 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). Valuation-based systems for Bayesian decision analysis. Operations Research, 40, 463-484.
• 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, 453-475.
• Shwe, M., Middleton, B., Heckerman, D., Henrion, M., Horvitz, E., Lehmann, H., & G. Cooper (1991). Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base I. The probabilistic model and inference algorithms. Methods of Information in Medicine, 30, 241-255.