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A causal mapping approach to constructing Bayesian networks
Open accessAbstract
This paper describes a systematic procedure for constructing Bayesian networks (BNs) from domain knowledge of experts using the causal mapping approach. We outline how causal knowledge of experts can be represented as causal maps, and how the graphical structure of causal maps can be modified to construct Bayes nets. Probability encoding techniques can be used to assess the numerical parameters of the resulting Bayes nets. We illustrate the construction of a Bayes net starting from a causal map of a systems analyst in the context of an information technology application outsourcing decision.
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Cited by
Bayesian networks in environmental modelling
Aguilera, P. A., Fernández, A., Fernández, R., Rumí, R., & Salmerón, A. (2011) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2011.06.004 · Cited by 666
Aurélio de Oliveira, M., Veriano Oliveira Dalla Valentina, L., & Possamai, O. (2012) · International Journal of Productivity and Performance Management · doi:10.1108/17410401211249201 · Cited by 59
Fuzzy Cognitive Maps for Engineering and Technology Management: What Works in Practice?
Jetter, A. (2006) · 2006 Technology Management for the Global Future - PICMET 2006 Conference · doi:10.1109/picmet.2006.296648 · Cited by 56
Metadata
- Authors
- Nadkarni, S., & Shenoy, P. P.
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2004-11
- Volume
- 38
- Issue
- 2
- Pages
- 259-281
- ISSN
- 0167-9236 (ISSN-L)
- OpenAlex
- W2114836920
- Cited by
- 293
- References
- 0
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- green
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- Nadkarni, S., & Shenoy, P. P. (2004). A causal mapping approach to constructing Bayesian networks. Decision Support Systems. https://doi.org/10.1016/s0167-9236(03)00095-2
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
