pyBMC: A General Bayesian Model Combination Package

pyBMC is a Python package for performing Bayesian Model Combination (BMC) on various predictive models. It provides tools for data handling, orthogonalization, Gibbs sampling, and prediction with uncertainty quantification.

Features

• Data Management: Load and preprocess nuclear mass data from HDF5 and CSV files
• Orthogonalization: Transform model predictions using Singular Value Decomposition (SVD)
• Bayesian Inference: Perform Gibbs sampling for model combination
• Uncertainty Quantification: Generate predictions with credible intervals
• Model Evaluation: Calculate coverage statistics for model validation

Installation

pip install pybmc

Quick Start

For a detailed walkthrough of how to use the package, please see the Usage Guide.

Documentation

Comprehensive documentation is available at https://ascsn.github.io/pybmc/, including:

• Usage Guide
• API Reference
• Theory Background
• Contribution Guidelines

Contributing

We welcome contributions! Please see our Contribution Guidelines for details on how to contribute to the project.

License

This project is licensed under the GPL-3.0 License - see the LICENSE file for details.

Citation

If you use pyBMC in your research, please cite:

@software{pybmc,
  title = {pyBMC: Bayesian Model Combination},
  author = {Kyle Godbey and Troy Dasher and An Le},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/ascsn/pybmc}}
}

Support

For questions or support, please open an issue on our GitHub repository.