

.. _sphx_glr_gallery_animation_bayes_update_sgskip.py:


================
The Bayes update
================

This animation displays the posterior estimate updates as it is refitted when
new data arrives.
The vertical line represents the theoretical value to which the plotted
distribution should converge.



.. code-block:: python


    # update a distribution based on new data.
    import numpy as np
    import matplotlib.pyplot as plt
    import scipy.stats as ss
    from matplotlib.animation import FuncAnimation


    class UpdateDist(object):
        def __init__(self, ax, prob=0.5):
            self.success = 0
            self.prob = prob
            self.line, = ax.plot([], [], 'k-')
            self.x = np.linspace(0, 1, 200)
            self.ax = ax

            # Set up plot parameters
            self.ax.set_xlim(0, 1)
            self.ax.set_ylim(0, 15)
            self.ax.grid(True)

            # This vertical line represents the theoretical value, to
            # which the plotted distribution should converge.
            self.ax.axvline(prob, linestyle='--', color='black')

        def init(self):
            self.success = 0
            self.line.set_data([], [])
            return self.line,

        def __call__(self, i):
            # This way the plot can continuously run and we just keep
            # watching new realizations of the process
            if i == 0:
                return self.init()

            # Choose success based on exceed a threshold with a uniform pick
            if np.random.rand(1,) < self.prob:
                self.success += 1
            y = ss.beta.pdf(self.x, self.success + 1, (i - self.success) + 1)
            self.line.set_data(self.x, y)
            return self.line,

    # Fixing random state for reproducibility
    np.random.seed(19680801)


    fig, ax = plt.subplots()
    ud = UpdateDist(ax, prob=0.7)
    anim = FuncAnimation(fig, ud, frames=np.arange(100), init_func=ud.init,
                         interval=100, blit=True)
    plt.show()

**Total running time of the script:** ( 0 minutes  0.000 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

    `Generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
