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.

翻译：贝叶斯网络（BNs）被广泛应用于建模、预测和决策等多个领域。pgmpy是一个Python软件包，提供了用于处理贝叶斯网络及相关模型的算法和工具集。它实现了结构学习、参数估计、近似推理与精确推理、因果推断以及模拟等算法。这些实现注重模块化和易于扩展性，允许用户快速修改或扩展现有算法，或针对不同用例实现新算法。pgmpy遵循MIT许可协议发布；源代码可在https://github.com/pgmpy/pgmpy获取，文档可在https://pgmpy.org查阅。