ModelDiscrepancy

A Bayesian framework for model-data comparison that accounts for theoretical uncertainties

Accurate comparisons between theoretical models and experimental data are critical for scientific progress. However, inferred physical model parameters can vary significantly with the chosen physics model, highlighting the importance of properly accounting for theoretical uncertainties. We present a Bayesian framework that explicitly quantifies these uncertainties by statistically modeling theory errors, guided by qualitative knowledge of a theory's varying reliability across the input domain.

For references, please see the following paper:

• Sunil Jaiswal, Chun Shen, Richard J. Furnstahl, Ulrich Heinz, Matthew T. Pratola, "Bayesian model-data comparison incorporating theoretical uncertainties", [https://arxiv.org/abs/2504.13144]

Directory and file descriptions

The repository is organized as follows:

.
├── src/
│   ├── ModelDiscrepancy.py
│   ├── sampling_methods.py
│   ├── get_quantiles.py
│   └── plot_scripts.py
├── tests/
│   ├── test_samplers.py
│   └── test_MD.py
├── projects/
│   └── [arXiv_identifier]/
└── environment.yml

• src/ : Contains the core Python source code for Bayesian inference methods.
  • ModelDiscrepancy.py : A class for computing the likelihood and posterior distributions of Bayesian models.
  • sampling_methods.py : A collection of functions for MCMC sampling.
  • get_quantiles.py : Functions to calculate quantiles from MCMC chains for various observables.
  • plot_scripts.py : Helper functions for generating informative plots, such as corner plots, from MCMC results.
• tests/ : A directory containing tests to check important components of the code.
  • test_samplers.py : Tests installation of all provided MCMC samplers.
  • test_MD.py : Tests installation of Model discrepancy class.
• projects/ : A directory for specific analysis projects. Subdirectory names are based on related arXiv paper identifiers. A README.md file in each subdirectory contains information on the workflow.
• environment.yml : Specifies all the necessary dependencies for setting up the project's development environment using Conda.

Installation and testing

Set up the required environment provided in environment.yml file with Conda.

conda env create -f environment.yml -n myenv
conda activate myenv

Run the following command to test important components of the code

python3 tests/test_samplers.py
python3 tests/test_MD.py

Cite this work

Please use the following BibTeX entry to cite this work:

@article{Jaiswal:2025hyp,
    author = "Jaiswal, Sunil and Shen, Chun and Furnstahl, Richard J. and Heinz, Ulrich and Pratola, Matthew T.",
    title = "{Bayesian model-data comparison incorporating theoretical uncertainties}",
    eprint = "2504.13144",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    doi = {10.1016/j.physletb.2025.139946},
    journal = {Physics Letters B},
    volume = {870},
    pages = {139946},
    year = {2025}
}

Contact details

Sunil Jaiswal (jaiswal.61@osu.edu)
Department of Physics
The Ohio State University
Columbus, Ohio 43210, USA