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Ross D. Shachter
Associate Professor
Management Science and Engineering

Office: Terman 416 | Phone: 650-723-4525 | Fax: 650-723-1614
Email: shachter @ stanford.edu
 
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., and 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., and Shortliffe, E. H. (1992). Patient-Specific Explanation in Models of Chronic Disease. AI in Medicine, 4(3), 191-205.
  12. Farr, B. R., and Shachter, R. D. (1992). Representation of Preferences in Decision Support Systems. Comput Biomed Res, 25(4), 324-335.
  13. Lehmann, H. P., and Shachter, R. D. (1994). A Physician-Based Architecture for the Construction and Use of Statistical Models. Meth Inform Med, 33, 423-32.
  14. Heckerman, D., and Shachter, R. (1995). Decision-Theoretic Foundations for Causal Reasoning. Journal of Artificial Intelligence Research, 3, 405-430.
  15. Owens, D. K., Shachter, R. D., and Nease, R. F. (1997). Representation and Analysis of Medical Decision Problems with Influence Diagrams. Medical Decision Making, 17(3, July-September), 241-262.
  16. Edwards, D. M., Shachter, R. D., and 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.
  17. Owens, D. K., Edwards, D. E., and Shachter, R. D. (1998). Population Effects of Preventive and Therapeutic HIV Vaccines in Early- and Late-Stage Epidemics. AIDS, 12(9), 1057-1066.
  18. Rupnow MF, Owens DK, Shachter R, Parsonnet J.Ê (1999). Helicobacter pylori vaccine development and use: a cost-effectiveness analysis using the Institute of Medicine methodology.Ê Helicobacter, 4, 272-280.
  19. Rupnow, M. F., Shachter, R. D., Owens, D. K., and Parsonnet, J. (2000). A Dynamic Transmission Model for Predicting Trends in Helicobacter pylori and Associated Diseases in the United States. Emerging Infectious Disease, 6(3), 228-237.
  20. Rupnow, M. F., Shachter, R. D., Owens, D. K., and Parsonnet, J. (2001). Quantifying the population impact of a prophylactic Helicobacter pylori vaccine. Vaccine, 20(5-6), 879-885.
  21. Burnside, E., Rubin, D., Shachter, R., Sohlich, R., and Sickles, E. (2004). A probabilistic expert system that provides automated mammographic-histologic correlation: Initial experience. AJRoentgenology, 182(2), 481-488.
  22. Scott, G., and Shachter, R. (2005). Individualizing Generic Decision Models Using Assessments as Evidence. Journal of Biomedical Informatics, 38(4), 281-297.
  23. Detwarasiti, A., and Shachter, R. D. (2005). Influence Diagrams for Team Decision Analysis. Decision Analysis, 4(2), 207-228.
  24. Burnside, E., Rubin, D., Fine, J., Shachter, R., Sisney, G., and Leung, W. (2006). Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience. Radiology 240: 666-673.
  25. Duriseti, R. S., R. D. Shachter, and Brandeau, M. L. (2006). Value of Quantitative Ddimer Assays in Identifying Pulmonary Embolism: Implications from a Sequential Decision Model. Academic Emergency Medicine 13(7): 755-766.

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, and 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., and 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., and 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., and 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., and 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., and 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., and 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., and 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., and 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., and 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., and 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.
  30. Burnside, B. E., Rubin, D. L., and Shachter, R. D. (2000). A Bayesian Network for Mammography. Paper presented at the AMIA Symposium 2000.
  31. Shapiro, D., Langley, P., and Shachter, R. (2001). Using background knowledge to speed reinforcement learning in physical agents. In Proceedings of the Fifth International Conference on Machine Learning, (pp. 254-261). Montreal: ACM Press.
  32. Shapiro, D., and Shachter, R. (2002). User-Agent Value Alignment. In Proceedings of the 2002 AAAI Symposium, Menlo Park: AAAI.
  33. Burnside, E., Rubin, D., and Shachter, R. (2004). Improving a Bayesian NetworkÕs Ability to Predict the Probability of Malignancy of Microcalcifications on Mammography. In Computer Assisted Radiology and Surgery, 1268 (pp. 1021-1026).
  34. Burnside, E., Rubin, D., and Shachter, R. (2004). Using a Bayesian Network to Predict the Probability and Type of Breast Cancer Represented by Microcalcifications on Mammography. In M. Fieschi, E. Coiera, and Y. Li (Eds.), Medinfo 2004: The 11th World Congress on Medical Informatics, (pp. 13-18). IOS Press.
  35. Bhattacharjya, D. and R. Shachter (2007). Evaluating influence diagrams with decision circuits. Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence. R. Parr and L. van der Gaag. Oregon, AUAI Press: 9-16.

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, and 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, and 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., and Peot, M. (1990). Simulation Approaches to General Probabilistic Inference on Belief Networks. In M. Henrion,R. D. Shachter,J. F. Lemmer, and 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., and Poh, K. L. (1991). Directed Reduction Algorithms and Decomposable Graphs. In P. Bonnisone,M. Henrion,L. N. Kanal, and J. F. Lemmer (Eds.), Uncertainty in Artificial Intelligence 6 (pp. 197-208). Amsterdam: North-Holland. (revised form of symposia publication 11)
  13. Rutledge, G., and Shachter, R. D. (1994). A method for the dynamic selection of models under time constraints. In P. Cheeseman and 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)
  14. Owens, D. K., Edwards, D. M., and Shachter, R. D. (2001). Costs and benefits of imperfect HIV vaccines: Implications for vaccine development and use. In E. H. Kaplan and R. Brookmeyer (Eds.), Quantitative Evaluation of HIV Prevention Programs (pp. 143-171). Yale Press.
  15. Owens, D., Edwards, D., Cavallaro, J., and Shachter, R. (2004). The Cost Effectiveness of Partially Effective HIV Vaccines. In M. Brandeau, F. Sainfort, and W. Pierskalla (Eds.), Operations Research and Health Care (pp. 403-418). Kluwer.
  16. Rubin, D., Burnsie, E., and Shachter, R. (2004). A Bayesian Network to Assist Mammography Interpretation. In M. Brandeau, F. Sainfort, and W. Pierskalla (Eds.), Operations Research and Health Care (pp. 695-702). Kluwer.
  17. Shachter, R. D. (2007). Model Building with Belief Networks and Influence Diagrams. Advances in Decision Analysis: From Foundations to Applications edited by W. Edwards, J. Ralph F. Miles and D. v. Winterfeldt, Cambridge University Press: 177-201.

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

School of Engineering Stanford University