<commit_before>.. pgmpy documentation master file, created by
   sphinx-quickstart on Tue Aug 30 18:17:42 2016.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

Welcome to pgmpy's documentation!
=================================

Getting Started:

.. toctree::
   :maxdepth: 3

   Installation <https://github.com/pgmpy/pgmpy#installation>

Basic Examples:

.. toctree::
   :maxdepth: 3

   Monte Hall Problem <https://github.com/pgmpy/pgmpy/blob/dev/examples/Monte%20Hall%20Problem.ipynb>
   Creating a Bayesian Network in pgmpy <https://github.com/pgmpy/pgmpy/blob/dev/examples/Creating%20a%20Bayesian%20Network.ipynb>
   Inference in Bayesian Network using Asia model <https://github.com/pgmpy/pgmpy/blob/dev/examples/Inference%20in%20Bayesian%20Networks.ipynb>
   Learning from Data <https://github.com/pgmpy/pgmpy/blob/dev/examples/Learning%20from%20data.ipynb>

Notebooks:

.. toctree::
   :maxdepth: 2

   Introduction to Probabilistic Graphical Models <https://nbviewer.jupyter.org/github/pgmpy/pgmpy_notebook/blob/master/notebooks/1.%20Introduction%20to%20Probabilistic%20Graphical%20Models.ipynb>
   Bayesian Networks <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/2.%20Bayesian%20Networks.ipynb>
   Markov Models <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/3.%20Markov%20Models.ipynb>
   Exact Inference in Graphical Models <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/4.%20Exact%20Inference%20in%20Graphical%20Models.ipynb>
   Approximate Inference in Graphical Models <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/5.%20Approximate%20Inference%20in%20Graphical%20Models.ipynb>
   Parameterizing with continuous variables <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/6.%20Parameterizing%20with%20Continuous%20Variables.ipynb>
   Sampling Algorithms <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/7.%20Sampling%20Algorithms.ipynb>
   Learning Bayesian Networks from data <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/9.%20Learning%20Bayesian%20Networks%20from%20Data.ipynb>
   Reading and writing files using pgmpy <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/8.%20Reading%20and%20Writing%20from%20pgmpy%20file%20formats.ipynb>

Documentation:

.. toctree::
   :maxdepth: 3

   models
   factors
   sampling
   inference
   readwrite
   estimators
   contributing

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

<commit_msg>changed link for Introduction to Probabilistic Graphical models<commit_after>.. pgmpy documentation master file, created by
   sphinx-quickstart on Tue Aug 30 18:17:42 2016.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

Welcome to pgmpy's documentation!
=================================

Getting Started:

.. toctree::
   :maxdepth: 3

   Installation <https://github.com/pgmpy/pgmpy#installation>

Basic Examples:

.. toctree::
   :maxdepth: 3

   Monte Hall Problem <https://github.com/pgmpy/pgmpy/blob/dev/examples/Monte%20Hall%20Problem.ipynb>
   Creating a Bayesian Network in pgmpy <https://github.com/pgmpy/pgmpy/blob/dev/examples/Creating%20a%20Bayesian%20Network.ipynb>
   Inference in Bayesian Network using Asia model <https://github.com/pgmpy/pgmpy/blob/dev/examples/Inference%20in%20Bayesian%20Networks.ipynb>
   Learning from Data <https://github.com/pgmpy/pgmpy/blob/dev/examples/Learning%20from%20data.ipynb>

Notebooks:

.. toctree::
   :maxdepth: 2

   Introduction to Probabilistic Graphical Models <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/1.%20Introduction%20to%20Probabilistic%20Graphical%20Models.ipynb>
   Bayesian Networks <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/2.%20Bayesian%20Networks.ipynb>
   Markov Models <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/3.%20Markov%20Models.ipynb>
   Exact Inference in Graphical Models <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/4.%20Exact%20Inference%20in%20Graphical%20Models.ipynb>
   Approximate Inference in Graphical Models <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/5.%20Approximate%20Inference%20in%20Graphical%20Models.ipynb>
   Parameterizing with continuous variables <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/6.%20Parameterizing%20with%20Continuous%20Variables.ipynb>
   Sampling Algorithms <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/7.%20Sampling%20Algorithms.ipynb>
   Learning Bayesian Networks from data <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/9.%20Learning%20Bayesian%20Networks%20from%20Data.ipynb>
   Reading and writing files using pgmpy <https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/8.%20Reading%20and%20Writing%20from%20pgmpy%20file%20formats.ipynb>

Documentation:

.. toctree::
   :maxdepth: 3

   models
   factors
   sampling
   inference
   readwrite
   estimators
   contributing

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`


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