pgmpy: A Python Toolkit for Bayesian Networks
- URL: http://arxiv.org/abs/2304.08639v1
- Date: Mon, 17 Apr 2023 22:17:53 GMT
- Title: pgmpy: A Python Toolkit for Bayesian Networks
- Authors: Ankur Ankan and Johannes Textor
- Abstract summary: pgmpy is a python package that implements algorithms for structure learning, parameter estimation, approximate and exact inference, causal inference, and simulations.
pgmpy is released under the MIT License.
- Score: 0.26651200086513094
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Bayesian Networks (BNs) are used in various fields for modeling, prediction,
and decision making. pgmpy is a python package that provides a collection of
algorithms and tools to work with BNs and related models. It implements
algorithms for structure learning, parameter estimation, approximate and exact
inference, causal inference, and simulations. These implementations focus on
modularity and easy extensibility to allow users to quickly modify/add to
existing algorithms, or to implement new algorithms for different use cases.
pgmpy is released under the MIT License; the source code is available at:
https://github.com/pgmpy/pgmpy, and the documentation at: https://pgmpy.org.
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