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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

The parameter uncertainty inflation fallacy.

Pascal Pernot¹

¹Laboratoire de Chimie Physique, UMR 8000, CNRS/Université Paris-Sud, F-91405 Orsay, France.

The Journal of Chemical Physics

|September 17, 2017

Summary

This summary is machine-generated.

Estimating prediction uncertainty for physical models is challenging due to model limitations. Parameter Uncertainty Inflation (PUI) methods are biased and do not accurately represent model inadequacy errors.

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Area of Science:

Computational chemistry
Statistical modeling
Physical models

Background:

Physical models often contain approximations, leading to prediction errors.
Estimating prediction uncertainty is crucial for reliable model application.
Existing methods struggle to account for model inadequacy.

Purpose of the Study:

To critically review Parameter Uncertainty Inflation (PUI) implementations.
To assess the bias of PUI in representing model inadequacy errors.
To evaluate the transferability of PUI for predicting other quantities.

Main Methods:

Critical review of PUI implementations in computational chemistry.
Analysis of PUI's ability to generate representative prediction uncertainties.
Assessment of PUI's bias concerning model inadequacy.

Main Results:

Parameter Uncertainty Inflation (PUI) is a method to adapt parameter uncertainty for prediction uncertainty.
Implementations of PUI in computational chemistry are shown to be biased.
PUI does not produce prediction uncertainty bands that conform to model inadequacy errors.

Conclusions:

Current PUI methods are biased and do not accurately capture model inadequacy.
Further development is needed to improve PUI for reliable uncertainty estimation.
The transferability advantage of PUI is limited by its inherent bias.