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Travis-CI:

# The Bayes updateΒΆ

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.

```# 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)

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