.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gallery/animation/bayes_update.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. meta:: :keywords: codex .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_gallery_animation_bayes_update.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. Output generated via `matplotlib.animation.Animation.to_jshtml`. .. GENERATED FROM PYTHON SOURCE LINES 13-72 .. container:: sphx-glr-animation .. raw:: html

.. code-block:: Python import math import matplotlib.pyplot as plt import numpy as np from matplotlib.animation import FuncAnimation def beta_pdf(x, a, b): return (x**(a-1) * (1-x)**(b-1) * math.gamma(a + b) / (math.gamma(a) * math.gamma(b))) class UpdateDist: 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, 10) 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 start(self): # Used for the *init_func* parameter of FuncAnimation; this is called when # initializing the animation, and also after resizing the figure. 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: self.success = 0 self.line.set_data([], []) return self.line, # Choose success based on exceed a threshold with a uniform pick if np.random.rand() < self.prob: self.success += 1 y = 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, init_func=ud.start, frames=100, interval=100, blit=True) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 8.193 seconds) .. _sphx_glr_download_gallery_animation_bayes_update.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: bayes_update.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: bayes_update.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_