.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples\2-examples\_aux_func.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_2-examples__aux_func.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_2-examples__aux_func.py:


Example of a Pyro model and inference
=====================================

.. GENERATED FROM PYTHON SOURCE LINES 6-8

.. code-block:: default

    # sphinx_gallery_thumbnail_number = -1








.. GENERATED FROM PYTHON SOURCE LINES 9-46

.. code-block:: default

    import pyro
    import pyro.distributions as dist
    from pyro.infer import MCMC, NUTS, Predictive
    import arviz as az


    def model(distributions_family, data):
        if distributions_family == "normal_distribution":
            mu = pyro.sample('$\mu$', dist.Normal(2.07, 0.07))
        elif distributions_family in "uniform_distribution":
            mu = pyro.sample('$\mu$', dist.Uniform(0, 10))
        else:
            raise ValueError("distributions_family must be either 'normal_distribution' or 'uniform_distribution'")
        sigma = pyro.sample('$\sigma$', dist.Gamma(0.3, 3))
        y = pyro.sample('$y$', dist.Normal(mu, sigma), obs=data)
        return y


    def infer_model(distributions_family, data):
        # 1. Prior Sampling
        prior = Predictive(model, num_samples=100)(distributions_family, data)
        # 2. MCMC Sampling
        nuts_kernel = NUTS(model)
        mcmc = MCMC(nuts_kernel, num_samples=1000, warmup_steps=100)  # Assuming 1000 warmup steps
        mcmc.run(distributions_family, data)
        # Get posterior samples
        posterior_samples = mcmc.get_samples(1100)
        # 3. Sample from Posterior Predictive
        posterior_predictive = Predictive(model, posterior_samples)(distributions_family, data)
        # %%
        az_data = az.from_pyro(
            posterior=mcmc,
            prior=prior,
            posterior_predictive=posterior_predictive
        )

        return az_data








.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.001 seconds)


.. _sphx_glr_download_examples_2-examples__aux_func.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




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

      :download:`Download Python source code: _aux_func.py <_aux_func.py>`

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

      :download:`Download Jupyter notebook: _aux_func.ipynb <_aux_func.ipynb>`


.. only:: html

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

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