Reporting Standards for Bayesian Network Modelling
Reproducibility is a key measure of the veracity of a modelling result or finding. In other research areas, notably in medicine, reproducibility is supported by mandating the inclusion of an agreed set of details into every research publication, facilitating systematic reviews, transparency and repr...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/1/69 |
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| author | Martine J. Barons Anca M. Hanea Steven Mascaro Owen Woodberry |
| author_facet | Martine J. Barons Anca M. Hanea Steven Mascaro Owen Woodberry |
| author_sort | Martine J. Barons |
| collection | DOAJ |
| description | Reproducibility is a key measure of the veracity of a modelling result or finding. In other research areas, notably in medicine, reproducibility is supported by mandating the inclusion of an agreed set of details into every research publication, facilitating systematic reviews, transparency and reproducibility. Governments and international organisations are increasingly turning to modelling approaches in the development and decision-making for policy and have begun asking questions about accountability in model-based decision making. The ethical issues of relying on modelling that is biased, poorly constructed, constrained by heroic assumptions and not reproducible are multiplied when such models are used to underpin decisions impacting human and planetary well-being. Bayesian Network modelling is used in policy development and decision support across a wide range of domains. In light of the recent trend for governments and other organisations to demand accountability and transparency, we have compiled and tested a reporting checklist for Bayesian Network modelling which will bring the desirable level of transparency and reproducibility to enable models to support decision making and allow the robust comparison and combination of models. The use of this checklist would support the ethical use of Bayesian network modelling for impactful decision making and research. |
| format | Article |
| id | doaj-art-306a10675ef44eeb8aa1e0d43edfd0ae |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-306a10675ef44eeb8aa1e0d43edfd0ae2025-01-24T13:31:53ZengMDPI AGEntropy1099-43002025-01-012716910.3390/e27010069Reporting Standards for Bayesian Network ModellingMartine J. Barons0Anca M. Hanea1Steven Mascaro2Owen Woodberry3Department of Statistics, University of Warwick, Coventry CV4 7AL, UKCentre of Excellence for Biosecurity Risk Analysis, University of Melbourne, Parkville, VIC 3052, AustraliaBayesian Intelligence, Upwey, VIC 3158, AustraliaBayesian Intelligence, Upwey, VIC 3158, AustraliaReproducibility is a key measure of the veracity of a modelling result or finding. In other research areas, notably in medicine, reproducibility is supported by mandating the inclusion of an agreed set of details into every research publication, facilitating systematic reviews, transparency and reproducibility. Governments and international organisations are increasingly turning to modelling approaches in the development and decision-making for policy and have begun asking questions about accountability in model-based decision making. The ethical issues of relying on modelling that is biased, poorly constructed, constrained by heroic assumptions and not reproducible are multiplied when such models are used to underpin decisions impacting human and planetary well-being. Bayesian Network modelling is used in policy development and decision support across a wide range of domains. In light of the recent trend for governments and other organisations to demand accountability and transparency, we have compiled and tested a reporting checklist for Bayesian Network modelling which will bring the desirable level of transparency and reproducibility to enable models to support decision making and allow the robust comparison and combination of models. The use of this checklist would support the ethical use of Bayesian network modelling for impactful decision making and research.https://www.mdpi.com/1099-4300/27/1/69Bayesian networksreproducibilitydecision supportsystematic reviewpolicyreporting standards |
| spellingShingle | Martine J. Barons Anca M. Hanea Steven Mascaro Owen Woodberry Reporting Standards for Bayesian Network Modelling Entropy Bayesian networks reproducibility decision support systematic review policy reporting standards |
| title | Reporting Standards for Bayesian Network Modelling |
| title_full | Reporting Standards for Bayesian Network Modelling |
| title_fullStr | Reporting Standards for Bayesian Network Modelling |
| title_full_unstemmed | Reporting Standards for Bayesian Network Modelling |
| title_short | Reporting Standards for Bayesian Network Modelling |
| title_sort | reporting standards for bayesian network modelling |
| topic | Bayesian networks reproducibility decision support systematic review policy reporting standards |
| url | https://www.mdpi.com/1099-4300/27/1/69 |
| work_keys_str_mv | AT martinejbarons reportingstandardsforbayesiannetworkmodelling AT ancamhanea reportingstandardsforbayesiannetworkmodelling AT stevenmascaro reportingstandardsforbayesiannetworkmodelling AT owenwoodberry reportingstandardsforbayesiannetworkmodelling |