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.. image:: logo.png :width: 250px :align: center :alt: logo | .. 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 | .. image:: https://img.shields.io/badge/consultation-book%20now-blue.svg :align: center :target: https://www.taggedweb.com/solution/one-hour-consultation-on-bayesian-networks-with-author-of-pgmpy?r=106&utm_source=badge&utm_medium=badge&utm_content=badge | .. toctree:: :maxdepth: 3 :hidden: :caption: Getting Started started/install.rst started/contributing.rst started/license.rst .. toctree:: :maxdepth: 3 :hidden: :caption: Base Structures base/base.rst .. toctree:: :maxdepth: 3 :hidden: :caption: Models models/bayesiannetwork.rst models/dbn.rst models/sem.rst models/naive.rst models/noisyor.rst models/markovnetwork.rst models/junctiontree.rst models/clustergraph.rst models/factorgraph.rst models/markovchain.rst .. toctree:: :maxdepth: 3 :hidden: :caption: Parameterization factors/discrete.rst factors/continuous.rst factors/discretize.rst .. toctree:: :maxdepth: 3 :hidden: :caption: Exact Inference exact_infer/ve.rst exact_infer/bp.rst exact_infer/causal.rst exact_infer/mplp.rst exact_infer/dbn_infer.rst exact_infer/model_testing.rst .. toctree:: :maxdepth: 3 :hidden: :caption: Approximate Inference approx_infer/approx_infer.rst approx_infer/bn_sampling.rst approx_infer/gibbs.rst .. toctree:: :maxdepth: 3 :hidden: :caption: Parameter Estimation param_estimator/mle.rst param_estimator/bayesian_est.rst param_estimator/em.rst param_estimator/sem_estimator.rst .. toctree:: :maxdepth: 3 :hidden: :caption: Structure Learning structure_estimator/pc.rst structure_estimator/hill.rst structure_estimator/tree.rst structure_estimator/mmhc.rst structure_estimator/exhaustive.rst .. toctree:: :maxdepth: 3 :hidden: :caption: Model Testing metrics/metrics.rst .. toctree:: :maxdepth: 3 :hidden: :caption: Input/Output readwrite/bif.rst readwrite/uai.rst readwrite/xmlbif.rst readwrite/pomdpx.rst readwrite/xmlbelief.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 - No - Yes Algorithms ========== .. csv-table:: :file: algorithms.csv :header-rows: 1 .. toctree:: :maxdepth: 1 :caption: Example Notebooks :numbered: examples/Earthquake.ipynb examples/Monty Hall Problem.ipynb examples/Creating a Discrete Bayesian Network.ipynb examples/Inference in Discrete Bayesian Networks.ipynb examples/Causal Games.ipynb examples/Causal Inference.ipynb examples/Learning Parameters in Discrete Bayesian Networks.ipynb examples/Structure Learning in Bayesian Networks.ipynb examples/Structure Learning with Chow-Liu.ipynb examples/Structure Learning with TAN.ipynb examples/Simulating Data.ipynb examples/Extending pgmpy.ipynb .. toctree:: :maxdepth: 1 :caption: Tutorial Notebooks :numbered: detailed_notebooks/1. Introduction to Probabilistic Graphical Models.ipynb detailed_notebooks/2. Bayesian Networks.ipynb detailed_notebooks/3. Causal Bayesian Networks.ipynb detailed_notebooks/4. Markov Models.ipynb detailed_notebooks/5. Exact Inference in Graphical Models.ipynb detailed_notebooks/6. Approximate Inference in Graphical Models.ipynb detailed_notebooks/7. Parameterizing with Continuous Variables.ipynb detailed_notebooks/8. Sampling Algorithms.ipynb detailed_notebooks/9. Reading and Writing from pgmpy file formats.ipynb detailed_notebooks/10. Learning Bayesian Networks from Data.ipynb detailed_notebooks/11. A Bayesian Network to model the influence of energy consumption on greenhouse gases in Italy.ipynb All example notebooks are also available at: https://github.com/pgmpy/pgmpy/tree/dev/examples All tutorial notebooks are also available at: https://github.com/pgmpy/pgmpy_notebook Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search`