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A methodology for developing Bayesian networks:: An application to information technology (IT) implementation
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DOI：10.1016/j.ejor.2006.01.016.png)
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摘要
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Bayesian Networks (BNs) are probabilistic inference engines that support reasoning under uncertainty. This article presents a methodology for building an information technology (IT) implementation BN from client-server survey data. The article also demonstrates how to use the BN to predict the attainment of IT benefits, given specific implementation characteristics (e.g., application complexity) and activities (e.g., reengineering). The BN is an outcome of a machine learning process that finds the network's structure and its associated parameters, which best fit the data. The article will be of interest to academicians who want to learn more about building BNs from real data and practitioners who are interested in IT implementation models that make probabilistic statements about certain implementation decisions. (c) 2006 Elsevier B.V. All rights reserved.
Keyword:
decision support systems
Bayesian networks
IT implementation
artificial intelligence
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IF:
6
论文数:
2.2W
被引数:
6.4W
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