Machine Learning Seminar presentation
Topic: Uncertainty precision and reliability of Ecohydrological models: Bayesian model selection
Speaker: Damian Mingo Ndiwago, University of Luxembourg, FSTM
Time: Wednesday, 2021.03.17, 10:00 CET
How to join: Please contact Jakub Lengiewicz
The Bayes factor (BF) is used in Bayesian model comparison and selection. Unlike information-theoretic approaches, it implicitly penalizes 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 specifications. It may favor 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.