A methodology for developing Bayesian networks: An application to information technology (IT) implementation

European Journal of Operational Research

Volumn 179, Issue 1, 2007, Pages 234-252

  Lauría, Eitel J M ^a   Duchessi, Peter J ^b  

^a MARIST COLLEGE   (United States)

^b STATE UNIVERSITY OF NEW YORK   (United States)

Author keywords

Artificial intelligence; Bayesian networks; Decision support systems; IT implementation

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLIENT SERVER COMPUTER SYSTEMS; DECISION SUPPORT SYSTEMS; INFORMATION TECHNOLOGY; LEARNING SYSTEMS;

BAYESIAN NETWORKS; IT IMPLEMENTATION; UNCERTAINTY;

PROBABILISTIC LOGICS;

EID: 33751319003     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2006.01.016     Document Type: Article

References (62)

1. Amari S.I. Differential Geometrical Methods in Statistics (1985), Springer-Verlag
2. Anderson R., and Vastag G. Causal modeling alternatives in operations research: Overview and application. European Journal of Operational Research 156 1 (2004) 92-109
3. Baronas A., and Louis R. Restoring sense of control during implementation: How user involvement leads to system acceptance. MIS Quarterly 12 1 (1988) 11-23
4. Bellman R. Adaptive Control Processes: A Guided Tour (1961), Princeton University Press
5. Benjamin R., and Levinson E. A framework for managing IT-enabled change. Sloan Management Review 34 4 (1993) 23-34
6. Berg L. The scoop of client-server costs. Computer World 26 46 (1992) 169-176
7. Bibi, S., Stamelos, I., 2004. Software process modeling with Bayesian belief networks. In: Proceedings of 10th International Software Metrics Symposium (Metrics 2004) 14-16 September 2004, Chicago, USA.
8. Brancheau J., Schuster L., and Mar S. Building and implementing an information systems architecture. Data Base 20 2 (1989) 9-17
9. Britt D.A. Conceptual Introduction to Modeling: Qualitative and Quantitative Perspectives (1997), Lawrence Erlbraum Associates Inc.
10. Cheney P., Mann R., and Amoroso D. Organizational factors affecting the success of end-user computing. Journal of Mgmt information Systems 3 1 (1986) 65-80
11. Chengalur-Smith I., and Duchessi P. Client-server implementation: Some management pointers. IEEE Transactions on Engineering Management 47 1 (2000)
12. Chin, W., 1998. Structural equation modeling in IS research. ISWorld Net Virtual Meeting Center at Temple University, 2-5 November 1998. Available from: .
13. Chwelos P., Benbasat I., and Dexter A. Research report: Empirical test of an EDI adoption model. Information Systems Research 12 3 (2001) 304-321
14. Cooper G., and Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9 4 (1992) 309-347
15. Cowell R., Dawid A., Lauritzen S., and Spiegelhalter D. Probabilistic Networks and Expert Systems (1999), Springer-Verlag
16. Cox J., Zmud R., and Clark S. Auditing an MRP System. Academy of Management Journal 24 2 (1981) 386-402
17. Dolan R. The end of delegation? Information technology and the CEO. Harvard Business Review 73 5 (1995) 166-168
18. Duchessi P., Schaninger C., and Hobbs D. Implementing a manufacturing, planning and control information system. California Management Review 31 3 (1989) 75-90
19. Ettlie J., Bridges W., and O'Keefe R. Organizational strategy and structural differences for radical versus incremental innovations. Management Science 30 6 (1984) 682-695
20. Fan C.-F., and Yu Y.-C. BBN-based software project risk management. Journal of Systems Software 73 (2004) 193-203
21. Fenton N.E., Krause P., and Neil M. Software measurement: Uncertainty and causal modelling. IEEE Software 10 4 (2002) 116-122
22. Fenton N.E., Marsh W., Neil M., Cates P., Forey S., and Tailor M. Making resource decisions for software projects. 26th International Conference on Software Engineering ICSE2004 May 2004, Edinburgh, United Kingdom (2004), IEEE Computer Society. 0-7695-2163-0 397-406
23. Griffith T., and Northcraft G. Cognitive elements in the implementation of new technology: Can less information provide more benefits?. MIS Quarterly 20 1 (1996) 99-111
24. Guidici P., and Castelo R. Improving MCMC model search for data mining. Machine Learning 50 (2003) 127-158
25. Hamilton S., and Chervany N. Evaluating information system effectiveness-Part I: Comparing evaluation approaches. MIS Quarterly 5 3 (1981) 55-69
26. Hastings W. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57 (1970) 97-109
27. Heckerman, D., Geiger, D., Chickering, D., 1994. Learning Bayesian networks: The combination of knowledge and statistical data. Technical Report MSR-TR-94-09, Microsoft Research.
28. Holland J. Adaptation in Natural and Artificial Systems (1975), The University of Michigan Press, Ann Arbor
29. IBM Consulting Group, 1994. Report on client/server, White Plains, NY.
30. In: Jordan M. (Ed). Learning in Graphical Models (1998), MIT Press
31. Keil M., Mann J., and Rai A. Why software projects escalate: An empirical analysis and test of four theoretical models. MIS Quarterly 24 4 (2000)
32. Kiernan, V., 1995. The Impact of Technology on Organizational Transformations. Available from: .
33. Kirkpatrick S., Gelatt D., and Vecchi M. Optimization by simulated annealing. Science 220 (1983) 671-680
34. Korb A., and Nicholson A. Bayesian Artificial Intelligence (2004), Chapman & Hall/CRC Press, UK
35. Lam W., and Bachus F. Learning Bayesian networks. An approach based on the MDL principle. Computational Iintelligence 10 3 (1994) 269-293
36. Lauría, E., 2005. Learning the structure of a Bayesian network: An application of information geometry and the minimum description length principle. In: Knuth, K.H., Abbas, A.E., Morris, R.D., Patrick Castle, J. (Eds.), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, pp. 293-301.
37. Lauritzen S.L. Causal inference in graphical models. In: Barndorff-Nielsen O.E., Cox D.R., and Kluppelberg C. (Eds). Complex Stochastic Systems (2000), Chapman & Hall, London
38. Lauritzen S., and Spiegelhalter D. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society Series B 50 2 (1988) 157-224
39. McKeen J. Successful development strategies for business application systems. MIS Quarterly 7 3 (1983) 47-65
40. Metropolis N., Rosenbluth A., Rosenbluth M., Teller A., and Teller E. Equations of State Calculations by Fast Computing Machines. Journal of Chemical Physics 21 (1953) 1087-1091
41. Neal, R., 1993. Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto.
42. Neil M., Fenton N.E., and Nielsen L. Building large-scale Bayesian networks. The Knowledge Engineering Review 15 3 (2000) 257-284
43. Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (1988), Morgan Kaufman Publishers, San Mateo, CA
44. Pearl J., and Verma T. A theory of inferred causation. Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference (1991), Morgan Kaufmann, San Mateo, CA
45. Ramoni M., and Sebastiani P. Bayesian methods for intelligent data analysis. In: Berthold M., and Hand D.J. (Eds). Intelligent Data Analysis: An Introduction (1999), Springer, New York, NY
46. Ravichandran T., and Rai A. Quality management in systems development: An organizational system perspective. MIS Quarterly 24 3 (2000)
47. Reich B., and Benbasat I. Factors that influence the social dimension of alignment between business and information technology objectives. MIS Quarterly 24 1 (2000) 81-111
48. Rissanen J. Stochastic complexity and modeling. Annals of Statistics 14 3 (1986) 1080-1100
49. Rissanen J. Fisher information and stochastic complexity. IEEE Transactions on Information Theory 42 (1996) 40-47
50. Robey D., and Farrow D. User involvement in information system development: A conflict model and empirical test. Management Science 28 1 (1982) 73-85
51. Rodríguez, C., 2001. Entropic priors for discrete probabilistic networks and for mixtures of Gaussian models. In: Mohammad-Djafari, A. (Ed.), Maximum Entropy and Bayesian Methods in Science and Engineering. Available from: .
52. Roweis S., and Saul L. Nonlinear dimensionality reduction by locally linear embedding. Science 290 (2000) 2323-2326
53. Russell S., and Norvig P. Artificial Intelligence, A Modern Approach. second ed. (2003), Prentice-Hall
54. Scheines, R., 1995. Bayesian estimation of Gaussian Bayes networks. In: Proceedings of NIPS 95, Workshop on Learning in Bayesian Networks and Other Graphical Models.
55. Schultheis R., and Bock D. Benefits and barriers to client/server computing. Journal of System Management 45 2 (1994) 12-41
56. Schwarz G. Estimating the dimension of a model. Annals of Statistics 6 (1978) 461-464
57. Spirtes P., Glymour C., and Scheines R. Causation, Prediction, and Search. second ed. (2000), MIT Press
58. Stamelos I., Angelisa L., Dimoua P., and Sakellaris P. On the use of Bayesian belief networks for the prediction of software productivity. Information and Software Technology 45 1 (2003) 51-60
59. Tenenbaum J., de Silva V., and Langford J. A global geometric framework for nonlinear dimensionality reduction. Science 290 (2000) 2319-2323
60. Witten I., and Frank E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (2000), Morgan Kaufmann Publishers
61. Wixom B., and Watson H. An Empirical Investigation of the Factors Affecting Data Warehousing Success. MIS Quarterly 25 1 (2001) 17-41
62. Wright S., and Wright A. Information system assurance for enterprise resource planning systems: Unique risk considerations. Journal of Information Systems 16 (2001) 99-113