| Code, Nr | Citation |
|---|---|
1 | NIL PublicationsBooks |
2 | NIL 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 |
3 | NIL Shachter, R. D.,Levitt, T. S.,Lemmer, J. F., and Kanal, L. N. (1990). Uncertainty in Artificial Intelligence 4. Amsterdam: North-Holland. |
4 | NIL 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 |
5 | NIL Shachter, R. D. (1986). Evaluating Influence Diagrams. Operations Research, 34(November-December), 871-882. |
6 | NIL Shachter, R. D. (1988). Probabilistic Inference and Influence Diagrams. Operations Research, 36(July-August), 589-605. |
7 | NIL 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. |
8 | NIL Shachter, R. D. and Kenley, C. R. (1989). Gaussian Influence Diagrams. Management Science, 35(May), 527-550. |
9 | NIL 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. |
10 | NIL Tatman, J. A. and Shachter, R. D. (1990). Dynamic Programming and Influence Diagrams. IEEE Transactions on Systems, Man and Cybernetics, 20(2), 365-379. |
11 | NIL Shachter, R. D. (1990). An Ordered Examination of Influence Diagrams. Networks, 20, 535-563. |
12 | NIL 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. |
13 | NIL Peot, M. A. and Shachter, R. D. (1991). Fusion and Propagation with Multiple Observations in Belief Networks. Artificial Intelligence, 48(3), 299-318. |
14 | NIL 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. |
15 | NIL 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. |
16 | NIL 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. |
17 | pubs/JAIRcaus.pdf Heckerman, D., & Shachter, R. (1995). Decision-Theoretic Foundations for Causal Reasoning. Journal of Artificial Intelligence Research, 3, 405-430. |
| j-jair-3-405 | Not available |
18 | NIL 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, in press. |
19 | NIL 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, in press. |
20 | NIL 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. Articles in Other Journals |
21 | NIL Shachter, R. D. and Heckerman, D. E. (1987). Thinking Backwards for Knowledge Acquisition. AI Magazine, 8(Fall), 55-61. Fully Refereed Symposia Publications |
22 | NIL Shachter, R. D. (1985). Intelligent Probabilistic Inference. Workshop on Uncertainty and Probability in Artificial Intelligence, UCLA, Los Angeles, 237-244. |
23 | NIL Shachter, R. D. (1986). DAVID: Influence Diagram Processing System for the Macintosh. Workshop on Uncertainty in Artificial Intelligence, University of Pennsylvania, Philadelphia, 243-248. |
24 | NIL 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. |
25 | NIL 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. |
26 | NIL 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. |
27 | NIL 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. |
28 | NIL Shachter, R. D. (1988). A Linear Approximation Method for Probabilistic Inference. Fourth Workshop on Uncertainty in Artificial Intelligence, University of Minnesota, Minneapolis, 299-306. |
29 | NIL Shachter, R. D. (1989). Evidence Absorption and Propagation through Evidence Reversals. Fifth Workshop on Uncertainty in Artificial Intelligence, University of Windsor, Ontario, 303-310. |
30 | NIL 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. |
31 | NIL 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. |
32 | NIL 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: |
33 | NIL 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. |
34 | NIL 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. |
35 | NIL 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. |
36 | NIL 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. |
37 | NIL 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. |
38 | NIL Poland, W B and R D Shachter (1993). Mixtures of Gaussians and Minimum Relative Entropy Techniques for Modeling Continuous Uncertainties: Uncertainty in Artificial Intelligence: Proceedings of the Ninth Conference (pp. 183-190). San Mateo, CA: Morgan Kaufmann. |
39 | NIL 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). |
40 | NIL 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. |
41 | NIL 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. |
42 | NIL 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. |
43 | NIL 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. |
44 | pubs/globcond.pdf 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. |
45 | NIL 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. |
46 | NIL 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. |
47 | pubs/UAIflex.pdf 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. |
48 | pubs/LearnNotSee.pdf 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. |
| c-uai-98-439 | Not available |
49 | pubs/bayesbl.pdf Shachter, R. D., & Mandelbaum, M. (1996). 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. Contributions to Books |
50 | NIL Shachter, R. D. (1983). An Incentive Approach to Eliciting Probabilities. Low Probability/High Consequence Risk Analysis (pp. 137-152). New York: Plenum Press. |
51 | NIL 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) |
52 | NIL 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) |
53 | NIL 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) |
54 | NIL 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) |
55 | NIL 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) |
56 | NIL 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) |
57 | NIL 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) |
58 | NIL 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) |
59 | NIL 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 |
60 | NIL 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) |
61 | NIL 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) |
62 | NIL 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 |
63 | NIL 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 |
64 | NIL 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 |