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Excursions in the Bayesian treatment of model error
L Mark Berliner1, Radu Herbei1, Christopher K Wikle2
1Department of Statistics, The Ohio State University, Columbus, OH, United States of America.
Plos One
|June 2, 2023
Summary
New methods address model errors in scientific research. These approaches improve data analysis by treating model outputs as observations, accounting for model inaccuracies to extract more reliable information.
Area of Science:
- Computational Science and Engineering
- Statistical Modeling
- Oceanography
Background:
- Advances in observational and computational tools have significantly improved scientific results.
- However, these advancements necessitate new research into managing and assessing the impacts of model errors.
Purpose of the Study:
- To propose novel methods for addressing model errors in scientific and engineering applications.
- To provide quantitative and simple approaches for extracting useful information from models while accounting for model error.
Main Methods:
- For manageable, physically-based statistical models, a stochastic 'model error process' is incorporated.
- For large-scale models where incorporating a model error process is impractical, dimension-reduced model output is treated as observational data.
- A data model with a bias component is used to represent the impacts of model error in the second case.
Main Results:
- The proposed methods offer valuable quantitative adjustments for model error.
- These techniques enable the extraction of more reliable information from model outputs.
- The methods were illustrated and assessed using an oceanographic problem.
Conclusions:
- The suggested approaches provide practical solutions for handling model errors in diverse scientific settings.
- These methods enhance the utility of scientific models by explicitly accounting for their inherent inaccuracies.
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