Uncertainty precision and reliability of Ecohydrological models: Bayesian model selection

Speaker: Damian Mingo Ndiwago (Faculty of Science, Technology and Medicine; University of Luxembourg)
Title: Uncertainty precision and reliability of Ecohydrological models: Bayesian model selection
Time: Wednesday, 2021.03.17, 10:00 a.m. (CET)
Place: fully virtual (contact Dr. Jakub Lengiewicz to register)
Format: 30 min. presentation + 30 min. discussion

Abstract: The Bayes factor (BF) is used in Bayesian model comparison and selection. Unlike information-theoretic approaches, it implicitly penalises the number of parameters in a model. BF can be used for both nested and non-nested models and is invariant to data transformation. Nevertheless, it is sensitive to prior parameter specification. It may favour a different model for weak prior distributions contrary to the frequentist methods of model selection. This phenomenon is known as Jeffreys-Lindley’s paradox. BF is undetermined when improper priors are used. However, the pseudo-Bayes (PsBF) is not affected by Jeffreys-Lindley’s paradox. Also, partial Bayes factors such as the Intrinsic Bayes factor (IBF) and the fractional Bayes factor (FBF) are determined for improper priors and are not affected by Lindley’s paradox. Thus, model selection should also report at least the PsBF. If the data set is large, the IBF and FBF should be reported. The IBF and the FBF are less sensitive to outliers.

I will introduce the research and show results based on synthetic data. Then, explain how this will be applied to (Eco)hydrological models with real data.