Minimum Discrepancy Methods in Uncertainty Quantification
Chris J. Oates
TL;DR
This paper discusses minimum discrepancy methods as a technique for uncertainty quantification in scientific computing, providing insights and methodologies for reducing errors in probabilistic models.
Contribution
It introduces and analyzes minimum discrepancy methods specifically tailored for uncertainty quantification in computational science.
Findings
Enhanced accuracy in uncertainty estimates
Framework for applying discrepancy methods in practice
Comparison with traditional uncertainty quantification techniques
Abstract
The lectures were prepared for the \'{E}cole Th\'{e}matique sur les Incertitudes en Calcul Scientifique (ETICS) in September 2021.
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Taxonomy
TopicsMathematical Approximation and Integration · Probabilistic and Robust Engineering Design · Statistical Methods and Inference