mbnpy 0.1.3

MBNpy is a Python package for Bayesian network applications for large-scale system events (i.e. high-dimensional data).

MBNpy

Overview

MBNpy is a Python toolkit for matrix-based Bayesian network (MBN)--an alternative data structure to conventional BN. MBN is designed to handle problems with a large number of parent nodes, where conventional BN tools often fall short. Example applications include transport networks and pipeline networks.

Contact

If you have discussion points, refer to the discussions tab.
If you have need support, refer to the issues tab.

Installation

Install using pip

MBNpy requires Python 3.12+. To install using pip, run:

pip install mbnpy

Downloading files from GitHub (development version)

git clone [e-mail]:jieunbyun/MBNpy.git
cd MBNpy

Documentation

For documentation, refer to the MBNpy docs. For research news and blog articles, refer to the MBNpy blog.

License

MBNpy is distributed under the MIT License

Copyright (c) <2025>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Referencing MBNPy

If you use this software for publication, please cite:

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