Publications

Books

  1. Eddy, D. M., Hasselblad, V., and Shachter, R. (1992). Meta-Analysis by the Confidence Profile Method: The Statistical Synthesis of Evidence . Boston: Academic Press.

Books Edited

  1. Shachter, R. D.,Levitt, T. S.,Lemmer, J. F., and Kanal, L. N. (1990). Uncertainty in Artificial Intelligence 4. Amsterdam: North-Holland.
  2. Henrion, M., Shachter, R. D., Lemmer, J. F., and Kanal, L. N. (1990). Uncertainty in Artificial Intelligence 5. Amsterdam: North-Holland.

Articles in Refereed Journals

  1. Shachter, R. D. (1986). Evaluating Influence Diagrams. Operations Research, 34(November-December), 871-882.
  2. Shachter, R. D. (1988). Probabilistic Inference and Influence Diagrams. Operations Research, 36(July-August), 589-605.
  3. Kent, D. J., Shachter, R. D., Sox, H. C., Ng, H. S., Shortliffe, L. D., Moynihan, S., and Torti, F. M. (1989). Efficient Scheduling of Cystoscopies in Monitoring for Recurrent Bladder Cancer. Medical Decision Making, 9(Jan-Mar), 26-39.
  4. Shachter, R. D. and Kenley, C. R. (1989). Gaussian Influence Diagrams. Management Science, 35(May), 527-550.
  5. Eddy, D. M., Hasselblad, V., and Shachter, R. D. (1990). An Introduction to a Bayesian Method for Meta-Analysis: The Confidence Profile Method. Medical Decision Making, 10(Jan-Mar), 15-23.
  6. Tatman, J. A. and Shachter, R. D. (1990). Dynamic Programming and Influence Diagrams. IEEE Transactions on Systems, Man and Cybernetics, 20(2), 365-379.
  7. Shachter, R. D. (1990). An Ordered Examination of Influence Diagrams. Networks, 20, 535-563.
  8. Eddy, D. M., Hasselblad, V., and Shachter, R. D. (1990). A Bayesian Method for Synthesizing Evidence: the Confidence Profile Method. International Journal of Technology Assessment in Health Care, 6, 31-55.
  9. Peot, M. A. and Shachter, R. D. (1991). Fusion and Propagation with Multiple Observations in Belief Networks. Artificial Intelligence, 48(3), 299-318.
  10. Kent, D. L.,Nease, R. A.,Sox, H. C.,Shortliffe, L. D., & Shachter, R. D. (1991). Evaluation of Nonlinear Optimization for Scheduling of Follow-up Cystocopies to Detect Recurrent Bladder Cancer. Med. Decn. Making, 11(4), 240-248.
  11. Jimison, H. B.,Fagan, L. M.,Shachter, R. D., & Shortliffe, E. H. (1992). Patient-Specific Explanation in Models of Chronic Disease. AI in Medicine, 4(3), 191-205.
  12. Lehmann, H. P., & Shachter, R. D. (1994). A Physician-Based Architecture for the Construction and Use of Statistical Models. Meth Inform Med, 33, 423-32.
  13. Heckerman, D., & Shachter, R. (1995). Decision-Theoretic Foundations for Causal Reasoning. Journal of Artificial Intelligence Research, 3, 405-430.
  14. Owens, D. K., Shachter, R. D., & Nease, R. F. (1997). Representation and Analysis of Medical Decision Problems with Influence Diagrams. Medical Decision Making, 17(3, July-September), 241-262.
  15. Edwards, D. M., Shachter, R. D., & Owens, D. K. (1998). A Dynamic Model of HIV Transmission for Evaluation of the Costs and Benefits of Vaccine Programs. Interfaces, 28(3), 144-166.
  16. Owens, D. K., Edwards, D. E., & Shachter, R. D. (1998). Population Effects of Preventive and Therapeutic HIV Vaccines in Early- and Late-Stage Epidemics. AIDS, 12(9), 1057-1066.

Articles in Other Journals

  1. Shachter, R. D. and Heckerman, D. E. (1987). Thinking Backwards for Knowledge Acquisition. AI Magazine, 8(Fall), 55-61.

Fully Refereed Symposia Publications

  1. Shachter, R. D. (1985). Intelligent Probabilistic Inference. Workshop on Uncertainty and Probability in Artificial Intelligence, UCLA, Los Angeles, 237-244.
  2. Shachter, R. D. (1986). DAVID: Influence Diagram Processing System for the Macintosh. Workshop on Uncertainty in Artificial Intelligence, University of Pennsylvania, Philadelphia, 243-248.
  3. Shachter, R. D. and Heckerman, D. E. (1986). A Backwards View for Assessment. Workshop on Uncertainty in Artificial Intelligence, University of Pennsylvania, Philadelphia, 237-242.
  4. Shachter, R. D., Eddy, D. M., Hasselblad, V., and Wolpert, R. (1987). A Heuristic Bayesian Approach to Knowledge Acquisition: Application to Analysis of Tissue-Type Plasminogen Activator. Third Workshop on Uncertainty in Artificial Intelligence,, University of Washington, Seattle, 229-236.
  5. Shachter, R. D. and Bertrand, L. J. (1987). Efficient Inference on Generalized Fault Diagrams. Third Workshop on Uncertainty in Artificial Intelligence, University of Washington, Seattle, 413-420.
  6. Shachter, R. D., Eddy, D. M., and Hasselblad, V. (1988). An Influence Diagram Approach to the Confidence Profile Method for Health Technology Assessment. Conference on Influence Diagrams for Decision Analysis, Inference and Prediction, University of California, Berkeley, 299-306.
  7. Shachter, R. D. (1988). A Linear Approximation Method for Probabilistic Inference. Fourth Workshop on Uncertainty in Artificial Intelligence, University of Minnesota, Minneapolis, 299-306.
  8. Shachter, R. D. (1989). Evidence Absorption and Propagation through Evidence Reversals. Fifth Workshop on Uncertainty in Artificial Intelligence, University of Windsor, Ontario, 303-310.
  9. Shachter, R. D. and Peot, M. (1989). Simulation Approaches to General Probabilistic Inference on Belief Networks. Fifth Workshop on Uncertainty in Artificial Intelligence, University of Windsor, Ontario, 311-318.
  10. Shachter, R. D., D'Ambrosio, B., and Del Favero, B. A. (1990). Symbolic Probabilistic Inference in Belief Networks. In Eighth National Conference on Artificial Intelligence, I (pp. 126-131). July 29-August 3, Boston: AAAI Press/The MIT Press.
  11. Shachter, R. D., Andersen, S. K., and Poh, K. L. (1990). Directed Reduction Algorithms and Decomposable Graphs. In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, (pp. 237-244). July 27-29, Cambridge, MA:
  12. Shachter, R. (1991). A Graph-Based Inference Method for Conditional Independence. In B. D'Ambrosio,P. Smets, & P. Bonissone (Eds.), Uncertainty in Artificial Intelligence: Proceedins of the Seventh Conference (pp. 353-360). San Mateo, CA: Morgan Kaufmann.
  13. Farr, B. R. and Shachter, R. D. (1992). Representation of Preferences in Decision Support Systems. Fifteenth Annual Symposium on Computer Applications in Medical Care (pp. 1018-1024). New York: McGraw-Hill.
  14. Chan, B. Y., & Shachter, R. D. (1992). Structural Controllability and Observability in Influence Diagrams. In Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference (pp. 25-32). San Mateo, CA: Morgan Kaufmann.
  15. Shachter, R. D., & Peot, M. A. (1992). Decision Making Using Probabilistic Inference Methods. In Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference (pp. 276-283). San Mateo, CA: Morgan Kaufmann.
  16. Lehmann, H P and R D Shachter (1993). End-User Construction of Influence Diagrams for Bayesian Statistics: Uncertainty in Artificial Intelligence: Proceedings of the Ninth Conference (pp. 48-54). San Mateo, CA: Morgan Kaufmann.
  17. Poland, W B and R D Shachter (1993). Mixtures of Gaussians and Minimum Relative Entropy Techniques for Modeling Continuous Uncertainties. In Uncertainty in Artificial Intelligence: Proceedings of the Ninth Conference (pp. 183-190). San Mateo, CA: Morgan Kaufmann.
  18. Rutledge, G and R D Shachter (1993). A Method for the Dynamic Selection of Models Under Time Constraints: Fourth International Workshop on Artificial Intelligence and Statistics in Ft. Lauderdale, FL, edited by Peter Cheeseman (pp. 459-468).
  19. Shachter, R D and P M Ndilikilikesha (1993). Using Potential Influence Diagrams for Probabilistic Inference and Decision Making: Uncertainty in Artificial Intelligence: Proceedings of the Ninth Conference (pp. 383-390). San Mateo, CA: Morgan Kaufmann.
  20. Azevedo-Filho, A., & Shachter, R. D. (1994). Laplace's Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables. In Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference (pp. 28-36). San Mateo, CA: Morgan Kaufmann.
  21. Heckerman, D. E., & Shachter, R. D. (1994). A Decision-Based View of Causality. In Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference (pp. 302-310). San Mateo, CA: Morgan Kaufmann.
  22. Poland, W. B., & Shachter, R. D. (1994). Three Approaches to Probability Model Selection. In Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference (pp. 478-483). San Mateo, CA: Morgan Kaufmann.
  23. Shachter, R. D., Andersen, S. K., & Szolovits, P. (1994). Global Conditioning for Probabilistic Inference in Belief Networks. In Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference (pp. 514-522). San Mateo, CA: Morgan Kaufmann.
  24. Chavez, T., & Shachter, R. D. (1995). Decision Flexibility. In Uncertainty in Artificial Intelligence: Proceedings of the Eleventh Conference (pp. to appear). San Mateo, CA: Morgan Kaufmann.
  25. Heckerman, D. E., & Shachter, R. D. (1995). A Definition and Graphical Representation for Causality. In Uncertainty in Artificial Intelligence: Proceedings of the Eleventh Conference (pp. 262-273). San Mateo, CA: Morgan Kaufmann.
  26. Shachter, R. D., & Mandelbaum, M. (1996). A Measure of Decision Flexibility. In Uncertainty in Artificial Intelligence: Proceedings of the Twelfth Conference (pp. 485-491). San Mateo, CA: Morgan Kaufmann.
  27. Peot, M. A., & Shachter, R. D. (1998). Learning from What You Don't Observe. In Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference (pp. 439-446). San Francisco, CA: Morgan Kaufmann.
  28. Shachter, R. D. (1998). Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams). In Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference (pp. 480-487). San Francisco, CA: Morgan Kaufmann.
  29. Shachter, R. D. (1999). Efficient Value of Informaton Computation. In Uncertainty in Artificial Intelligence: Proceedings of the Fifteenth Conference (in press). San Francisco, CA: Morgan Kaufmann.

Contributions to Books

  1. Shachter, R. D. (1983). An Incentive Approach to Eliciting Probabilities. Low Probability/High Consequence Risk Analysis (pp. 137-152). New York: Plenum Press.
  2. Shachter, R. D. (1986). Intelligent Probabilistic Inference. In L. N. Kanal and J. F. Lemmer (Ed.), Uncertainty in Artificial Intelligence (pp. 371-382). Amsterdam: North-Holland. (revised form of symposia publication 1)
  3. Shachter, R. D. (1986). Evaluating Influence Diagrams. In A. Basu (Ed.), Reliability and Quality Control (pp. 321-344). Amsterdam: North-Holland. (revised form of journal article 1)
  4. Shachter, R. D. and Heckerman, D. E. (1988). A Backwards View for Assessment. In J. F. Lemmer and L. N. Kanal (Ed.), Uncertainty in Artificial Intelligence 2 (pp. 317-324). Amsterdam: North-Holland. (revised form of symposia publication 2)
  5. Shachter, R. D. (1988). DAVID: Influence Diagram Processing System for the Macintosh. In J. F. Lemmer and L. N. Kanal (Ed.), Uncertainty in Artificial Intelligence 2 (pp. 191-196). Amsterdam: North-Holland. (revised form of symposia publication 3)
  6. Shachter, R. D., Eddy, D. M., Hasselblad, V., and Wolpert, R. (1989). A Heuristic Bayesian Approach to Knowledge Acquisition: Application to the Analysis of Tissue-Type Plasminogen Activator. In L. N. Kanal, T. S. Levitt, and J. F. Lemmer (Ed.), Uncertainty in Artificial Intelligence 3 (pp. 183-190). Amsterdam: North-Holland. (revised form of symposia publication 4)
  7. Shachter, R. D. and Bertrand, L. J. (1989). Efficient Inference on Generalized Fault Diagrams. In L. N. Kanal, T. S. Levitt, and J. F. Lemmer (Ed.), Uncertainty in Artificial Intelligence 3 (pp. 325-332). Amsterdam: North-Holland. (revised form of symposia publication 5)
  8. Shachter, R. D., Eddy, D. M., and Hasselblad, V. (1990). An Influence Diagram Approach to Medical Technology Assessment. In R. M. Oliver and J. Q. Smith (Ed.), Influence Diagrams, Belief Nets, and Decision Analysis (pp. 321-350). Chichester: Wiley. (revised form of symposia publication 6)
  9. Shachter, R. D. (1990). A Linear Approximation Method for Probabilistic Inference. In R. D. Shachter,T. S. Levitt,J. F. Lemmer, & L. N. Kanal (Eds.), Uncertainty in Artificial Intelligence 4 (pp. 93-103). Amsterdam: North-Holland. (revised form of symposia publication 7)
  10. Shachter, R. D. (1990). Evidence Absorption and Propagation through Evidence Reversals. In M. Henrion,R. D. Shachter,J. F. Lemmer, & L. N. Kanal (Eds.), Uncertainty in Artificial Intelligence 5 (pp. 173-190). Amsterdam: North-Holland. (revised form of symposia publication 8
  11. Shachter, R. D., & Peot, M. (1990). Simulation Approaches to General Probabilistic Inference on Belief Networks. In M. Henrion,R. D. Shachter,J. F. Lemmer, & L. N. Kanal (Eds.), Uncertainty in Artificial Intelligence 5 (pp. 221-230). Amsterdam: North-Holland. (revised form of symposia publication 9)
  12. Shachter, R. D.,Andersen, S. K., & Poh, K. L. (1991). Directed Reduction Algorithms and Decomposable Graphs. In P. Bonnisone,M. Henrion,L. N. Kanal, & J. F. Lemmer (Eds.), Uncertainty in Artificial Intelligence 6 (pp. 197-208). Amsterdam: North-Holland. (revised form of symposia publication 11)
  13. Rutledge, G., & Shachter, R. D. (1994). A method for the dynamic selection of models under time constraints. In P. Cheeseman & R. W. Oldford (Eds.), Selecting Models from Data: Artificial Intelligence and Statistics IV (pp. 79-88). New York: Springer-Verlag. (revised form of symposia publication 18)

Research Software Published

  1. Shachter, R. D. and Bertrand, L. J. (1987). DAVID, Influence Diagram Processing System for the Macintosh. Duke University Center for Academic Computing, initial release, December 1987; Updated release, August 1988.

Dissertation

  1. Shachter, R. D. (1982). The Economics of a Difference of Opinion: An Incentive Approach to Eliciting Probabilities. Ph.D. Thesis, Department of Industrial Engineering and Operations Research, University of California, Berkeley. Ten Selected Publications