Sensitivity analysis in influence diagrams

The influence diagram framework serves as a powerful modeling tool for symmetric decision problems with a single decision maker. However, one of the main difficulties when representing decision problems using influence diagrams is eliciting the utilities and the probabilities. This makes it desirable to be able to investigate: 1) how sensitive the solution is to variations in some utility or probability parameter, and 2) how robust the solution is to joint variations over a set of parameters. In this paper, we propose a general algorithm for performing these types of analysis.

[1]  Frank Jensen,et al.  From Influence Diagrams to junction Trees , 1994, UAI.

[2]  Richard Gonzalez,et al.  Curvature of the Probability Weighting Function , 1996 .

[3]  Anders L. Madsen,et al.  Lazy Evaluation of Symmetric Bayesian Decision Problems , 1999, UAI.

[4]  Gregory F. Cooper,et al.  A Bayesian Method for Constructing Bayesian Belief Networks from Databases , 1991, UAI.

[5]  Kathryn B. Laskey Sensitivity analysis for probability assessments in Bayesian networks , 1995, IEEE Trans. Syst. Man Cybern..

[6]  Prakash P. Shenoy,et al.  Valuation-Based Systems for Bayesian Decision Analysis , 1992, Oper. Res..

[7]  Serafín Moral,et al.  Penniless propagation in join trees , 2000 .

[8]  Daphne Koller,et al.  Utilities as Random Variables: Density Estimation and Structure Discovery , 2000, UAI.

[9]  Enrique F. Castillo,et al.  Sensitivity analysis in discrete Bayesian networks , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[10]  Finn Verner Jensen,et al.  Myopic Value of Information in Influence Diagrams , 1997, UAI.

[11]  Dennis Nilsson,et al.  Probabilities of Future Decisions , 2000 .

[12]  Linda C. van der Gaag,et al.  Making Sensitivity Analysis Computationally Efficient , 2000, UAI.

[13]  Concha Bielza,et al.  Sensitivity Analysis in IctNeo , 2000 .

[14]  Ross D. Shachter,et al.  Dynamic programming and influence diagrams , 1990, IEEE Trans. Syst. Man Cybern..

[15]  B. McNeil,et al.  Probabilistic Sensitivity Analysis Using Monte Carlo Simulation , 1985, Medical decision making : an international journal of the Society for Medical Decision Making.

[16]  Ronald A. Howard,et al.  Readings on the Principles and Applications of Decision Analysis , 1989 .

[17]  Eric Horvitz,et al.  Reasoning about the Value of Decision-Model Refinement: Methods and Application , 1993, UAI.

[18]  Steffen L. Lauritzen,et al.  Evaluating Influence Diagrams using LIMIDs , 2000, UAI.

[19]  Veerle Coupé,et al.  Sensitivity Analysis of Decision-Theoretic Networks , 2000 .

[20]  Claus Skaanning A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters , 2000, UAI.

[21]  A. Hadi,et al.  A new method for efficient symbolic propagation in discrete Bayesian networks , 1996 .

[22]  G. Hazen,et al.  Do Sensitivity Analyses Really Capture Problem Sensitivity? An Empirical Analysis Based on Information Value , 1999 .

[23]  Ross D. Shachter Efficient Value of Information Computation , 1999, UAI.

[24]  Ross D. Shachter Evaluating Influence Diagrams , 1986, Oper. Res..

[25]  Thomas D. Nielsen,et al.  Welldefined Decision Scenarios , 1999, UAI.

[26]  Gordon B. Hazen,et al.  Sensitivity Analysis and the Expected Value of Perfect Information , 1998, Medical decision making : an international journal of the Society for Medical Decision Making.

[27]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.