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

The ndtools utilities can be installed either as a released package from PyPI or in editable (developer) mode from the source repository.

Using pip (recommended for users)

Install the latest released version from PyPI:

pip install tbnpy

This installs a stable version of the tools suitable for general use.

Verify the installation:

python -c "import tbnpy; print(tbnpy.__version__)"

Using pip (editable / developer install)

If you are developing tbnpy or working directly with the source code, install in editable mode from the repository root:

# Activate your environment first (if using conda)
conda activate <your-env>

# Install in editable mode
pip install -e .

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.

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