U.S. Department of EnergyOffice of Scientific and Technical Information
Model Calibration with Markov Chain Monte Carlo Tutorial
Abstract
The purpose of this tutorial is to demonstrate how to use Markov chain Monte Carlo (MCMC) to calibrate a model. By calibration, we mean the selection of model parameters (and, when relevant, structures). A common goal in model development and diagnostics is calibration, or the identification of model structures and parameters which are consistent with data. While models can be calibrated through hand-tuning parameters or minimizing simple error metrics such as root-mean-square-error (RMSE), these approaches can underrepresent the probabilistic nature of the data-generating process, as well as the potential for multiple model configurations to be consistent with the data. Probabilistic uncertainty quantification, which is the topic of this notebook, can address these concerns. This tutorial is presented as an appendix to the e-book: Addressing Uncertainty in MultiSector Dynamics Research.
- Publication Date:
- Research Org.:
- MultiSector Dynamics - Living, Intuitive, Value-adding, Environment
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Subject:
- Markov chain Monte Carlo; Model Calibration
- OSTI Identifier:
- 2565322
- DOI:
- https://doi.org/10.57931/2565322
Citation Formats
Srikrishnan, Vivek. Model Calibration with Markov Chain Monte Carlo Tutorial. United States: N. p., 2025.
Web. doi:10.57931/2565322.
Srikrishnan, Vivek. Model Calibration with Markov Chain Monte Carlo Tutorial. United States. doi:https://doi.org/10.57931/2565322
Srikrishnan, Vivek. 2025.
"Model Calibration with Markov Chain Monte Carlo Tutorial". United States. doi:https://doi.org/10.57931/2565322. https://www.osti.gov/servlets/purl/2565322. Pub date:Mon May 12 00:00:00 EDT 2025
@article{osti_2565322,
title = {Model Calibration with Markov Chain Monte Carlo Tutorial},
author = {Srikrishnan, Vivek},
abstractNote = {The purpose of this tutorial is to demonstrate how to use Markov chain Monte Carlo (MCMC) to calibrate a model. By calibration, we mean the selection of model parameters (and, when relevant, structures). A common goal in model development and diagnostics is calibration, or the identification of model structures and parameters which are consistent with data. While models can be calibrated through hand-tuning parameters or minimizing simple error metrics such as root-mean-square-error (RMSE), these approaches can underrepresent the probabilistic nature of the data-generating process, as well as the potential for multiple model configurations to be consistent with the data. Probabilistic uncertainty quantification, which is the topic of this notebook, can address these concerns. This tutorial is presented as an appendix to the e-book: Addressing Uncertainty in MultiSector Dynamics Research.},
doi = {10.57931/2565322},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon May 12 00:00:00 EDT 2025},
month = {Mon May 12 00:00:00 EDT 2025}
}