Bayesian networks offer a mechanism for diagnosing the key changes necessary for system improvement and for predicting the impacts of potential change actions. A review of the fundamentals of Bayesian networks is presented, with a discussion of strengths and weaknesses. A model is constructed to assess the impact of potential changes in the decision-making process of a large global manufacturing organization. The procedure for building a network structure, estimating conditional probabilities, assessing internal consistency, and conducting probabilistic inference are provided by the application.
Get full access to this article
View all access options for this article.
References
1.
Bernardo, J., & Smith, A. (1994). Bayesian theory. New York: Wiley.
2.
Castillo, E., Guterrez, J., & Hadi, A. (1997). Expert systems and probabilistic network models. New York: Springer-Verlag.
3.
Chickering, D., Geiger, D., & Heckerman, D. (1994). Learning Bayesian networks in NP-hard (Tech. Rep. MSR-TR-94-17). Seattle, WA: Microsoft Research.
4.
Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues for field settings. Chicago: Rand McNally.
5.
Cooper, G. (1997). A simple constraint-based algorithm for efficiently mining observational databases for causal relationships. Journal of Data Mining and Knowledge Discovery, 1, 203-224.
6.
Cooper, G. (1999). An overview of the representation and discovery of causal relationships using Bayesian networks. In C. Glymour & G. Cooper (Eds.), Computation, causation & discovery (pp. 3-62). Menlo Park, CA: MIT Press.
7.
Cooper, G., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic network from data. Machine Learning, 9, 309-347.
8.
Cowell, R. (1999). Introduction to inference for Bayesian networks. In M. I. Jordan (Ed.), Learning in graphical models (pp. 9-49). Cambridge, MA: MIT Press.
9.
Cowell, R., Dawid, A. P., Lauritzen, S. L., & Spiegelhalter, D. J. (1999). Probabilistic networks and expert systems. New York: Springer-Verlag.
10.
Dawid, A. P. (1992). Applications of a general propagation algorithm of probabilistic expert systems. Statistics and Computing, 2, 25-36.
11.
Geiger, D., & Herkerman, D. (1997). A characterization of Dirichlet distributions through local and global independence. Annals of Statistics, 25, 1344-1368.
12.
Heckerman, D. (1999). A tutorial on learning with Bayesian networks. In M. I. Jordan (Ed.), Learning in graphical models (pp. 301-354). Cambridge, MA: MIT Press.
13.
Heckerman, D., Geiger, D., & Chickering, D. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20, 197-243.
14.
Jensen, F. (1996). An introduction to Bayesian networks. London: University College London Press.
15.
Jensen, F., Lauritzen, S. L., & Olesen, K. G. (1990). Bayesian updating in causal probabilistic networks by local computation. Computational Statistics Quarterly, 4, 269-282.
16.
Kass, R., & Raftery, A. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773-795.
17.
Kiiveri, H., & Speed, T. (1982). Structural analysis of multivariate data: A review. In S. Leinhardt (Ed.), Sociological methodology (pp. 209-289). San Francisco: Jossey Bass.
18.
Lauritzen, S. L., & Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their applications to expert systems (with discussion). Journal of Royal Statistical Society, Series B, 50, 157-224.
19.
Neapolitan, N. (1990). Probabilistic reasoning in expert systems. New York: John Wiley.
20.
Pearl, J. (1988). Probabilistic inference in intelligent systems. San Mateo, CA: Morgan Kaufmann.
21.
Pearl, J. (1996). Structural and probabilistic causality. In D. R. Shanks, K. J. Holyoak, & D. L. Medin (Eds.), The psychology of learning and motivation (Vol. 34, pp. 393-435). San Diego, CA: Academic Press.
22.
Pearl, J. (1998). Graphs, causality, and structural equation models. Sociological Methods and Research, 27, 226-284.
23.
Ramoni, M., & Sebastiani, P. (1998). Bayesian knowledge discoverer. London: Knowledge Media Institute, The Open University.
24.
Ramoni, M., & Sebastiani, P. (1999). Bayesian methods for intelligent data analysis. In M. Bethold & D. J. Hand (Eds.), An introduction to intelligent data analysis (pp. 129-166). New York: Springer-Verlag.
25.
Russell, S. J., & Norvig, P. (1995). Artificial intelligence: A modern approach. Englewood Cliffs, NJ: Prentice-Hall.
26.
Scheines, R., Spirtes, P., Glymour, C., & Meek, C. (1994). TETRAD II: User’s manual. Hillsdale, NJ: Lawrence Erlbaum.
27.
Shafer, G. R., & Pearl, J. (Eds.). (1990). Readings in uncertain reasoning. San Mateo, CA: Morgan Kaufmann.
28.
Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, prediction, and search. New York: Springer-Verlag.
29.
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, Series B, 36, 111-147.
30.
Stone, M. (1977). Asymptotics for and against cross-validation. Biometrika, 64, 29-35.
