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- Title
Holistic Process Models: A Bayesian Predictive Ensemble Method for Single and Coupled Unit Operation Models.
- Authors
Montano Herrera, Liliana; Eilert, Tobias; Ho, I-Ting; Matysik, Milena; Laussegger, Michael; Guderlei, Ralph; Schrantz, Bernhard; Jung, Alexander; Bluhmki, Erich; Smiatek, Jens
- Abstract
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.
- Subjects
MARKOV chain Monte Carlo; PREDICTION models; BAYESIAN field theory
- Publication
Processes, 2022, Vol 10, Issue 4, pN.PAG
- ISSN
2227-9717
- Publication type
Article
- DOI
10.3390/pr10040662