Uncertainty Analysis by Bayesian Inference. Arhonditsis, G., Kim, D., Kelly, N., Neumann, A., & Javed, A. In Recknagel, F. & Michener, W. K., editors, Ecological Informatics: Data Management and Knowledge Discovery, pages 215–249. Springer International Publishing, Cham, 2018. ![Uncertainty Analysis by Bayesian Inference [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The scientific methodology of mathematical models and their credibility to form the basis of public policy decisions have been frequently challenged. The development of novel methods for rigorously assessing the uncertainty underlying model predictions is one of the priorities of the modeling community. Striving for novel uncertainty analysis tools, we present the Bayesian calibration of process-based models as a methodological advancement that warrants consideration in ecosystem analysis and biogeochemical research. This modeling framework combines the advantageous features of both process-based and statistical approaches; that is, mechanistic understanding that remains within the bounds of data-based parameter estimation. The incorporation of mechanisms improves the confidence in predictions made for a variety of conditions, whereas the statistical methods provide an empirical basis for parameter value selection and allow for realistic estimates of predictive uncertainty. Other advantages of the Bayesian approach include the ability to sequentially update beliefs as new knowledge is available, the rigorous assessment of the expected consequences of different management actions, the optimization of the sampling design of monitoring programs, and the consistency with the scientific process of progressive learning and the policy practice of adaptive management. We illustrate some of the anticipated benefits from the Bayesian calibration framework, well suited for stakeholders and policy makers when making environmental management decisions, using the Hamilton Harbour and the Bay of Quinte—two eutrophic systems in Ontario, Canada—as case studies.
@incollection{arhonditsis_uncertainty_2018,
address = {Cham},
title = {Uncertainty {Analysis} by {Bayesian} {Inference}},
isbn = {978-3-319-59928-1},
url = {https://doi.org/10.1007/978-3-319-59928-1_11},
abstract = {The scientific methodology of mathematical models and their credibility to form the basis of public policy decisions have been frequently challenged. The development of novel methods for rigorously assessing the uncertainty underlying model predictions is one of the priorities of the modeling community. Striving for novel uncertainty analysis tools, we present the Bayesian calibration of process-based models as a methodological advancement that warrants consideration in ecosystem analysis and biogeochemical research. This modeling framework combines the advantageous features of both process-based and statistical approaches; that is, mechanistic understanding that remains within the bounds of data-based parameter estimation. The incorporation of mechanisms improves the confidence in predictions made for a variety of conditions, whereas the statistical methods provide an empirical basis for parameter value selection and allow for realistic estimates of predictive uncertainty. Other advantages of the Bayesian approach include the ability to sequentially update beliefs as new knowledge is available, the rigorous assessment of the expected consequences of different management actions, the optimization of the sampling design of monitoring programs, and the consistency with the scientific process of progressive learning and the policy practice of adaptive management. We illustrate some of the anticipated benefits from the Bayesian calibration framework, well suited for stakeholders and policy makers when making environmental management decisions, using the Hamilton Harbour and the Bay of Quinte—two eutrophic systems in Ontario, Canada—as case studies.},
language = {en},
urldate = {2020-01-03},
booktitle = {Ecological {Informatics}: {Data} {Management} and {Knowledge} {Discovery}},
publisher = {Springer International Publishing},
author = {Arhonditsis, George and Kim, Dong-Kyun and Kelly, Noreen and Neumann, Alex and Javed, Aisha},
editor = {Recknagel, Friedrich and Michener, William K.},
year = {2018},
doi = {10.1007/978-3-319-59928-1_11},
pages = {215--249},
}
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