pybmc Documentation

Welcome to the official documentation for pybmc, a Python package for general Bayesian Model Combination (BMC).

Overview

pybmc provides a comprehensive framework for combining multiple predictive models using Bayesian statistics. Key features include:

• Data Management: Load and preprocess various types of 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

Getting Started

Installation

pip install pybmc

Quick Start

For a detailed walkthrough, please see the Usage Guide.

Documentation Contents

• Usage Guide: Detailed examples and tutorials
• API Reference: Complete documentation of all classes and functions
• Theory Background: Mathematical foundations of Bayesian model combination
• Contributing: How to contribute to pybmc

Support

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

License

This project is licensed under the MIT License - see the License file for details.