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.. toctree::
   :maxdepth: 2
   :hidden:

   started/base.rst
   examples.rst
   models/base.rst
   factors/base.rst
   infer/base.rst
   causal_infer/base.rst
   param_estimator/base.rst
   structure_estimator/base.rst
   metrics/metrics.rst
   readwrite/base.rst
   plotting.rst
   tutorial.rst

pgmpy is a Python package for causal inference and probabilistic inference
using Directed Acyclic Graphs (DAGs) and Bayesian Networks with a focus on
modularity and extensibility. Implementations of various algorithms for Causal
Discovery (a.k.a, Structure Learning), Parameter Estimation, Approximate
(Sampling Based) and Exact inference, and Causal Inference are available.

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.. figure:: pgmpy_workflow.png
   :alt: Possible Workflows in pgmpy for Directed Acyclic Graphs (DAGs) and Bayesian Networks (BNs).

   Possible Workflows in pgmpy for Directed Acyclic Graphs (DAGs) and Bayesian Networks (BNs).

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Supported Data Types
====================

.. list-table::
   :header-rows: 1

   * -
     - Causal Discovery
     - Parameter Estimation
     - Causal Inference
     - Probabilistic Inference
     - Simulations
   * - **Categorical**
     - Yes
     - Yes
     - Yes
     - Yes
     - Yes
   * - **Continuous**
     - Yes
     - Yes
     - Yes (partial)
     - Yes
     - Yes
   * - **Mixed**
     - Yes
     - No
     - No
     - No
     - Yes
   * - **Time Series**
     - No
     - Yes
     - Yes (ApproximateInference)
     - Yes
     - Yes

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Algorithms
==========

.. csv-table::
   :file: algorithms.csv
   :header-rows: 1

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Examples
========

**Example notebooks:** :doc:`examples`

**Tutorial notebooks:** :doc:`tutorial`

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Citation
========
If you use pgmpy in your scientific work, please consider citing us:

.. code-block:: text

   Ankur Ankan, & Johannes Textor (2024). pgmpy: A Python Toolkit for Bayesian Networks. Journal of Machine Learning Research, 25(265), 1–8.

Bibtex:

.. code-block:: text

   @article{Ankan2024,
     author  = {Ankur Ankan and Johannes Textor},
     title   = {pgmpy: A Python Toolkit for Bayesian Networks},
     journal = {Journal of Machine Learning Research},
     year    = {2024},
     volume  = {25},
     number  = {265},
     pages   = {1--8},
     url     = {http://jmlr.org/papers/v25/23-0487.html}
   }