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.. image:: https://github.com/pgmpy/pgmpy/actions/workflows/ci.yml/badge.svg?branch=dev :target: https://github.com/pgmpy/pgmpy/actions?query=branch%3Adev .. image:: https://codecov.io/gh/pgmpy/pgmpy/branch/dev/graph/badge.svg :target: https://codecov.io/gh/pgmpy/pgmpy .. image:: https://api.codacy.com/project/badge/Grade/78a8256c90654c6892627f6d8bbcea14 :target: https://www.codacy.com/gh/pgmpy/pgmpy?utm_source=github.com&utm_medium=referral&utm_content=pgmpy/pgmpy&utm_campaign=Badge_Grade .. image:: https://img.shields.io/pypi/dm/pgmpy.svg :target: https://pypistats.org/packages/pgmpy .. image:: https://badges.gitter.im/Join%20Chat.svg :target: https://gitter.im/pgmpy/pgmpy?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge .. toctree:: :maxdepth: 2 :hidden: started/base.rst base/base.rst models/base.rst factors/base.rst exact_infer/base.rst exact_infer/model_testing.rst approx_infer/base.rst param_estimator/base.rst structure_estimator/base.rst metrics/metrics.rst readwrite/base.rst examples.rst tutorial.rst pgmpy is a pure python implementation for Bayesian Networks with a focus on modularity and extensibility. Implementations of various alogrithms for Structure Learning, Parameter Estimation, Approximate (Sampling Based) and Exact inference, and Causal Inference are available. Supported Data Types ==================== .. list-table:: :header-rows: 1 * - - Structure Learning - Parameter Estimation - Causal Inference - Probabilistic Inference * - **Discrete** - Yes - Yes - Yes - Yes * - **Continuous** - Yes (only PC) - No - Yes (partial) - No * - **Hybrid** - No - No - No - No * - **Time Series** - No - Yes - Yes (ApproximateInference) - Yes Algorithms ========== .. csv-table:: :file: algorithms.csv :header-rows: 1 Example notebooks are also available at: https://github.com/pgmpy/pgmpy/tree/dev/examples Tutorial notebooks are also available at: https://github.com/pgmpy/pgmpy_notebook Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search`