tbnpy 0.1.0

Tensor-based Bayesian Network (TBN) toolkit for scalable probabilistic inference

tbnpy

tbnpy is a Python toolkit for tensor-based Bayesian networks (TBNs), designed for scalable probabilistic inference in systems with large, structured state spaces.
It is particularly suited to applications where classical Bayesian network implementations struggle due to combinatorial growth in system states.

The package provides:

• A tensor-based formulation of Bayesian networks
• Scalable exact and approximate inference algorithms
• Support for hybrid discrete–continuous models
• Reusable system-level rules and probabilistic components
• Examples illustrating end-to-end modelling and inference workflows

Documentation

📘 Full documentation is available here:
👉 https://jieunbyun.github.io/tbnpy/

The online documentation includes:

• Conceptual overview of tensor-based Bayesian networks
• Installation and getting-started guides
• API reference for all core modules
• Worked examples (including the ABCDE example)
• Repository structure and design rationale

Repository structure

tbnpy/
├── tbnpy/              # Core library
├── examples/           # Worked examples and case studies
├── docs/               # Sphinx documentation source
├── .github/            # CI/CD workflows (GitHub Actions)
└── README.md

Installation

For development use, clone the repository and install in editable mode:

git clone https://github.com/jieunbyun/tbnpy.git
cd tbnpy
pip install -e .

Dependencies required for documentation are listed in requirements.txt.

Development status

tbnpy is under active development.
The API may evolve as new modelling patterns, inference strategies, and large-scale case studies are incorporated.

Citation

Citation

A dedicated tbnpy methodology paper is currently under preparation.
In the meantime, please cite the following related work:

Byun, J.-E., & Song, J. (2021). Generalized matrix-based Bayesian network for multi-state systems. Reliability Engineering & System Safety, 211, 107468.

License

This project is released under an MIT license.
See the repository for license details.

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For detailed usage, examples, and API documentation, please refer to:
🔗 https://jieunbyun.github.io/tbnpy/