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Risk analysis : an official publication of the Society for Risk Analysis
06, 2022;42 (6): 1149-1154.

Bayesian networks for risk analysis and decision support.

Anca M Hanea, Annemarie Christophersen, Sandra Alday
Author Information
  1. Anca M Hanea: Centre of Excellence for Biosecurity Risk Analysis, University of Melbourne, Melbourne, Victoria, Australia.
  2. Annemarie Christophersen: GNS Science, Lower Hutt, Wellington, New Zealand.
  3. Sandra Alday: University of Sydney, Sydney, New South Wales, Australia.
PMID: 35770819 DOI: 10.1111/risa.13938

Abstract

No abstract text available.

References

  1. Aven, T., & Flage, R. (2020). Foundational challenges for advancing the field and discipline of risk analysis. Risk Analysis, 40(S1), 2128-2136.
  2. Barons, M., Mascaro, S., & Hanea, A. (2021). Balancing the elicitation burden and the richness of expert input when quantifying discrete Bayesian networks. Risk Analysis, https://doi.org/10.1111/risa.13772
  3. Burgman, M., Layman, H., & French, S. (2021). Eliciting model structures for multivariate probabilistic risk analysis. Frontiers in Applied Mathematics and Statistics, 7, https://www.frontiersin.org/article/10.3389/fams.2021.668037
  4. Choi, T., & Lambert, J. (2017). Advances in risk analysis with big data. Risk Analysis, 37(8), 1435-1442.
  5. Christophersen, A., Bailey, L., & Cavana, R. (2020). Risk analysis meets Bayesian network modelling in Wellington, Aotearoa, New Zealand, focusing on responsible and culturally appropriate decision-making. IFORS News, 14, 17-18.
  6. Cooke, R., Joe, H., & Chang, B. (2021). Vine regression with Bayes nets: A critical comparison with traditional approaches based on a case study on the effects of breastfeeding on IQ. Risk Analysis, https://doi.org/10.1111/risa.13695
  7. Fenton, N., & Neil, M. (2013). Risk assessment and decision analysis with Bayesian networks. CRC Press.
  8. Govender, I., Sahlin, U., & O'Brien, G. (2021). Bayesian network applications for sustainable holistic water resources management: Modeling opportunities for South Africa. Risk Analysis, https://doi.org/10.1111/risa.13798
  9. Greenberg, M., Cox, A., Bier, V., Lambert, J., Lowrie, K., North, W., Siegrist, M., & Wu, F. (2020). Risk analysis: Celebrating the accomplishments and embracing ongoing challenges. Risk Analysis, 40(S1), 2113-2127.
  10. Greenberg, M., Haas, C., Cox, A., Lowrie, K., McComas, K., & North, W. (2012). Ten most important accomplishments in risk analysis, 1980-2010. Risk Analysis, 32(5), 771-781.
  11. Hanea, A., Hilton, Z., Knight, B., & Robinson, A. (2022). Co-designing and building an expert-elicited non-parametric Bayesian network model: Demonstrating a methodology using a Bonamia Ostreae spread risk case study. Risk Analysis, https://doi.org/10.1111/risa.13904
  12. Hart, B., & Pollino, C. (2008). Increased use of Bayesian network models will improve ecological risk assessments. Human and Ecological Risk Assessment: An International Journal, 14(5), 851-853.
  13. ISO. (2018). Risk management-Guidelines (ISO 31000:2018). Technical report, International Organization for Standardization.
  14. Jamieson, L., Woodberry, O., Mascaro, S., Meurisse, N., Jaksons, R., Brown, S., & Ormsby, M. (2021). An integrated biosecurity risk assessment model (IBRAM) for evaluating the risk of import pathways for the establishment of invasive species. Risk Analysis, https://doi.org/10.1111/risa.13861
  15. Jensen, F. (1996). An introduction to Bayesian networks. UCL Press.
  16. Kaikkonen, L., Parviainen, T., Rahikainen, M., Uusitalo, L., & Lehikoinen, A. (2021). Bayesian networks in environmental risk assessment: A review. Integrated Environmental Assessment and Management, 17(1), 62-78.
  17. Kaplan, S., & Garrick, B. (1981). On the quantitative definition of risk. Risk Analysis, 1(1), 11-27.
  18. Korb, K., & Nicholson, A. (2010). Bayesian artificial intelligence (2nd ed.). CRC Press, Inc.
  19. Linstone, H., & Turoff, M. (1975). The Delphi method: Techniques and applications. Addison-Wesley.
  20. Mascaro, S., & Woodberry, O. (2022). A flexible method for parameterising ranked nodes in Bayesian networks using Beta distributions. Risk Analysis, (in press).
  21. Meurisse, N., Marcot, B., Woodberry, O., Barratt, B., & Todd, J. (2021). Risk analysis frameworks used in biological control and introduction of a novel Bayesian network tool. Risk Analysis, https://doi.org/10.1111/risa.13812
  22. Moe, S., Carriger, J., & Glendell, M. (2021). Increased use of Bayesian network models has improved environmental risk assessments. Integrated Environmental Assessment and Management, 17(1), 53-61.
  23. Nateghi, R., & Aven, T. (2021). Risk analysis in the age of big data: The promises and pitfalls. Risk Analysis, 41(10), 1751-1758.
  24. Nyberg, E., Nicholson, A., Korb, K., Wybrow, M., Zukerman, I., Mascaro, S., Thakur, S., Oshni Alvandi, A., Riley, J., Pearson, R., Morris, S., Herrmann, M., Azad, A., Bolger, F., Hahn, U., & Lagnado, D. (2021). BARD: A structured technique for group elicitation of Bayesian networks to support analytic reasoning. Risk Analysis, https://doi.org/10.1111/risa.13759
  25. Pearl, J. (1986). Fusion, propagation, and structuring in belief networks. Artificial Intelligence, 29(3), 241-288.
  26. Pourret, O., Naim, P., & Marcot, B. (2008). Bayesian networks: A practical guide to applications. Wiley.
  27. Ruiz-Tagle, A., Lopez Droguett, E., & Groth, K. (2021). Exploiting the capabilities of Bayesian networks for engineering risk assessment: Causal reasoning through interventions. Risk Analysis, https://doi.org/10.1111/risa.13711
  28. Simsekler, M., & Qazi, A. (2020). Adoption of a data-driven Bayesian belief network investigating organizational factors that influence patient safety. Risk Analysis, https://doi.org/10.1111/risa.13610

MeSH Term

Bayes Theorem
Decision Support Techniques
Risk Assessment
Editorial Research Support, Non-U.S. Gov't

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Created with Highcharts 10.0.0Bayesiannetworksriskanalysisdecisionsupport

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