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http://dx.doi.org/10.18419/opus-14948
Autor(en): | Höge, Marvin Guthke, Anneli Nowak, Wolfgang |
Titel: | Bayesian model weighting : the many faces of model averaging |
Erscheinungsdatum: | 2020 |
Dokumentart: | Zeitschriftenartikel |
Seiten: | 16 |
Erschienen in: | Water 12 (2020), No. 309 |
URI: | http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-149677 http://elib.uni-stuttgart.de/handle/11682/14967 http://dx.doi.org/10.18419/opus-14948 |
ISSN: | 2073-4441 |
Zusammenfassung: | Model averaging makes it possible to use multiple models for one modelling task, like predicting a certain quantity of interest. Several Bayesian approaches exist that all yield a weighted average of predictive distributions. However, often, they are not properly applied which can lead to false conclusions. In this study, we focus on Bayesian Model Selection (BMS) and Averaging (BMA), Pseudo-BMS/BMA and Bayesian Stacking. We want to foster their proper use by, first, clarifying their theoretical background and, second, contrasting their behaviours in an applied groundwater modelling task. We show that only Bayesian Stacking has the goal of model averaging for improved predictions by model combination. The other approaches pursue the quest of finding a single best model as the ultimate goal, and use model averaging only as a preliminary stage to prevent rash model choice. Improved predictions are thereby not guaranteed. In accordance with so-called ℳ-settings that clarify the alleged relations between models and truth, we elicit which method is most promising. |
Enthalten in den Sammlungen: | 02 Fakultät Bau- und Umweltingenieurwissenschaften |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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water-12-00309-v3.pdf | 1,14 MB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons