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05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Sensitivity analysis of uncertainty in model prediction.
Trent Russi1, Andrew Packard, Ryan Feeley
1Department of Mechanical Engineering, University of California, Berkeley, California 94720-1740, USA. trussi@berkeley.edu
The Journal of Physical Chemistry. A
|February 29, 2008
Summary
Data Collaboration enhances model prediction by analyzing uncertainty from experimental data and parameters. This framework guides experimental and theoretical strategies for scientific community action.
Area of Science:
- Computational Chemistry
- Chemical Engineering
- Systems Biology
Background:
- Data Collaboration is a framework for model-based inference from experimental data.
- Previous applications include chemical kinetics, reaction system consistency, and model discrimination.
Purpose of the Study:
- To advance Data Collaboration by incorporating sensitivity analysis of prediction uncertainty.
- To assess the impact of experimental and parameter uncertainty on model predictions.
Main Methods:
- Sensitivity analysis of uncertainty was developed and evaluated alongside the Data Collaboration optimization.
- Sensitivity coefficients were calculated to quantify contributions to prediction uncertainty.
Main Results:
- Identified key contributors to uncertainty in model predictions.
- Ranked the effects of different experimental and parameter uncertainties.
- Integrated uncertainty analysis with cost reduction strategies.
Conclusions:
- The developed sensitivity analysis provides crucial insights into uncertainty propagation.
- This method aids in selecting optimal experimental and theoretical strategies.
- It guides community action by prioritizing uncertainty reduction efforts.
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