Print Email Facebook Twitter Bayesian networks for identifying incorrect probabilistic intuitions in a climate trend uncertainty quantification context Title Bayesian networks for identifying incorrect probabilistic intuitions in a climate trend uncertainty quantification context Author Hanea, A.M. (University of Melbourne) Nane, G.F. (TU Delft Applied Probability) Wielicki, B.A. (NASA Langley Research Center) Cooke, R.M. (Resources for the Future) Date 2018 Abstract Probabilistic thinking can often be unintuitive. This is the case even for simple problems, let alone the more complex ones arising in climate modelling, where disparate information sources need to be combined. The physical models, the natural variability of systems, the measurement errors and their dependence upon the observational period length should be modelled together in order to understand the intricacies of the underlying processes. We use Bayesian networks (BNs) to connect all the above-mentioned pieces in a climate trend uncertainty quantification framework. Inference in such models allows us to observe some seemingly nonsensical outcomes. We argue that they must be pondered rather than discarded until we understand how they arise. We would like to stress that the main focus of this paper is the use of BNs in complex probabilistic settings rather than the application itself. Subject Bayesian networksClimate sensitivitydiscordant agreementnegative learningobsolescence To reference this document use: http://resolver.tudelft.nl/uuid:a8f2eeba-fa12-4963-ac0c-9750d9bdf13b DOI https://doi.org/10.1080/13669877.2018.1437059 ISSN 1366-9877 Source Journal of Risk Research, 1-16 Bibliographical note Accepted Author Manuscript Part of collection Institutional Repository Document type journal article Rights © 2018 A.M. Hanea, G.F. Nane, B.A. Wielicki, R.M. Cooke Files PDF 43042021.pdf 903.17 KB Close viewer /islandora/object/uuid:a8f2eeba-fa12-4963-ac0c-9750d9bdf13b/datastream/OBJ/view