:orphan:



.. _sphx_glr_auto_examples:

.. _basic_examples:

Example Gallery
---------------

These are some simple examples demonstrating ``spn``.


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    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_language:

.. _dsl_examples:

Syntax and DSL
--------------

Examples that demonstrate the basic syntax for composing Sum-Product Networks.



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    <div class="sphx-glr-thumbcontainer" tooltip="We start by creating an SPN. Using a Domain-Specific Language (DSL), we can quickly create an S...">

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 .. figure:: /auto_examples/language/images/thumb/sphx_glr_plot_spn_dsl_thumb.png
     :alt: Domain Specific Language for SPNs

     :ref:`sphx_glr_auto_examples_language_plot_spn_dsl.py`

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    </div>


.. toctree::
   :hidden:

   /auto_examples/language/plot_spn_dsl

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    <div class="sphx-glr-thumbcontainer" tooltip="The alternative to the DSL is to construct SPNs directly from Sum and Product objects, demonstr...">

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 .. figure:: /auto_examples/language/images/thumb/sphx_glr_plot_spn_object_hierarchy_thumb.png
     :alt: Composing SPNs from Sums and Products

     :ref:`sphx_glr_auto_examples_language_plot_spn_object_hierarchy.py`

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    </div>


.. toctree::
   :hidden:

   /auto_examples/language/plot_spn_object_hierarchy
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    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_models:

.. _model_examples:

Tractable Probabilistic Models
------------------------------

These examples demonstrate some of the tractable probabilistic models which
can be represented, learned, and queried in SPFlow.



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    <div class="sphx-glr-thumbcontainer" tooltip="This demonstrates learning a Mixed Sum-Product Network (MSPN) where the data is composed of var...">

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 .. figure:: /auto_examples/models/images/thumb/sphx_glr_mixed_spn_thumb.png
     :alt: Mixed SPN

     :ref:`sphx_glr_auto_examples_models_mixed_spn.py`

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    </div>


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   :hidden:

   /auto_examples/models/mixed_spn

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    <div class="sphx-glr-thumbcontainer" tooltip="The setup is similar to the Mixed SPN, but here we learn a parametric SPN. ">

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 .. figure:: /auto_examples/models/images/thumb/sphx_glr_parametric_spn_thumb.png
     :alt: Parametric SPN

     :ref:`sphx_glr_auto_examples_models_parametric_spn.py`

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    </div>


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   :hidden:

   /auto_examples/models/parametric_spn

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    <div class="sphx-glr-thumbcontainer" tooltip="We can perform classification by learning an SPN from data and then comparing the probabilities...">

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 .. figure:: /auto_examples/models/images/thumb/sphx_glr_plot_learn_spn_classifier_thumb.png
     :alt: Learning an SPN for Classification

     :ref:`sphx_glr_auto_examples_models_plot_learn_spn_classifier.py`

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    </div>


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   :hidden:

   /auto_examples/models/plot_learn_spn_classifier

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    <div class="sphx-glr-thumbcontainer" tooltip="We can learn a SPN with multivariate leaf. This example demonstrates learning an SPN with Chow ...">

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 .. figure:: /auto_examples/models/images/thumb/sphx_glr_multivariate_leaf_thumb.png
     :alt: Multivariate Leaf

     :ref:`sphx_glr_auto_examples_models_multivariate_leaf.py`

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    </div>


.. toctree::
   :hidden:

   /auto_examples/models/multivariate_leaf

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    <div class="sphx-glr-thumbcontainer" tooltip="With SPFlow we can learn both the structure and the parameters of CNets, a particular kind of S...">

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 .. figure:: /auto_examples/models/images/thumb/sphx_glr_cnets_thumb.png
     :alt: Cutset Networks (CNets)

     :ref:`sphx_glr_auto_examples_models_cnets.py`

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    </div>


.. toctree::
   :hidden:

   /auto_examples/models/cnets
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    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_queries:

.. _probabilistic_queries:

Querying Probabilistic Models
-----------------------------

One of the most interesting aspect of SPNs is the tractable inference.
These examples demonstrate some of the ways these models may be queried.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="&quot;Marginalizing an SPN&quot; means summing out all other non-relevant variables. ">

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 .. figure:: /auto_examples/queries/images/thumb/sphx_glr_plot_marginalize_thumb.png
     :alt: Marginalizing an SPN

     :ref:`sphx_glr_auto_examples_queries_plot_marginalize.py`

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    </div>


.. toctree::
   :hidden:

   /auto_examples/queries/plot_marginalize

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This shows how to perform marginal inference on Sum-Product Networks.">

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 .. figure:: /auto_examples/queries/images/thumb/sphx_glr_plot_tractable_inference_thumb.png
     :alt: Tractable Marginal Inference

     :ref:`sphx_glr_auto_examples_queries_plot_tractable_inference.py`

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    </div>


.. toctree::
   :hidden:

   /auto_examples/queries/plot_tractable_inference
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    <div class="sphx-glr-clear"></div>



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 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-gallery


  .. container:: sphx-glr-download sphx-glr-download-python

    :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

    :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`


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 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_