pybmc 0.2.2

A package for Bayesian model combination

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. The model combination methodology follows this paper by Giuliani et al.

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 Pablo Giuliani 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.