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http://dx.doi.org/10.18419/opus-12591
Autor(en): | Montano Herrera, Liliana Eilert, Tobias Ho, I-Ting Matysik, Milena Laussegger, Michael Guderlei, Ralph Schrantz, Bernhard Jung, Alexander Bluhmki, Erich Smiatek, Jens |
Titel: | Holistic process models : a Bayesian predictive ensemble method for single and coupled unit operation models |
Erscheinungsdatum: | 2022 |
Dokumentart: | Zeitschriftenartikel |
Seiten: | 18 |
Erschienen in: | Processes 10 (2022), No. 662 |
URI: | http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-126100 http://elib.uni-stuttgart.de/handle/11682/12610 http://dx.doi.org/10.18419/opus-12591 |
ISSN: | 2227-9717 |
Zusammenfassung: | The coupling of individual models in terms of end-to-end calculations for unit operations in manufacturing processes is a challenging task. We present a probability distribution-based approach for the combined outcomes of parametric and non-parametric models. With this so-called Bayesian predictive ensemble, the statistical moments such as mean value and standard deviation can be accurately computed without any further approximation. It is shown that the ensemble of different model predictions leads to an uninformed prior distribution, which can be transformed into a predictive posterior distribution using Bayesian inference and numerical Markov Chain Monte Carlo calculations. We demonstrate the advantages of our method using several numerical examples. Our approach is not restricted to certain unit operations, and can also be used for the more robust interpretation and assessment of model predictions in general. |
Enthalten in den Sammlungen: | 08 Fakultät Mathematik und Physik |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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processes-10-00662.pdf | 895,72 kB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons