.. _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) .. only :: html .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: bayes_update_sgskip.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: bayes_update_sgskip.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_