# Example of a Binomial distribution¶

Figure 3.9.

This shows an example of a binomial distribution with various parameters. We’ll generate the distribution using:

dist = scipy.stats.binom(...)

Where … should be filled in with the desired distribution parameters Once we have defined the distribution parameters in this way, these distribution objects have many useful methods; for example:

• dist.pmf(x) computes the Probability Mass Function at values x in the case of discrete distributions

• dist.pdf(x) computes the Probability Density Function at values x in the case of continuous distributions

• dist.rvs(N) computes N random variables distributed according to the given distribution

Many further options exist; refer to the documentation of scipy.stats for more details.

# Author: Jake VanderPlas
# License: BSD
#   The figure produced by this code is published in the textbook
#   "Statistics, Data Mining, and Machine Learning in Astronomy" (2013)
#   For more information, see http://astroML.github.com
#   To report a bug or issue, use the following forum:
#    https://groups.google.com/forum/#!forum/astroml-general
import numpy as np
from scipy.stats import binom
from matplotlib import pyplot as plt

#----------------------------------------------------------------------
# This function adjusts matplotlib settings for a uniform feel in the textbook.
# Note that with usetex=True, fonts are rendered with LaTeX.  This may
# result in an error if LaTeX is not installed on your system.  In that case,
# you can set usetex to False.
if "setup_text_plots" not in globals():
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=True)

#------------------------------------------------------------
# Define the distribution parameters to be plotted
n_values = [20, 20, 40]
b_values = [0.2, 0.6, 0.6]
linestyles = ['-', '--', ':']
x = np.arange(-1, 200)

#------------------------------------------------------------
# plot the distributions
fig, ax = plt.subplots(figsize=(5, 3.75))

for (n, b, ls) in zip(n_values, b_values, linestyles):
# create a binomial distribution
dist = binom(n, b)

plt.plot(x, dist.pmf(x), color='black', linestyle='steps-mid' + ls,
label=r'\$b=%.1f,\ n=%i\$' % (b, n))

plt.xlim(-0.5, 35)
plt.ylim(0, 0.25)

plt.xlabel('\$x\$')
plt.ylabel(r'\$p(x|b, n)\$')
plt.title('Binomial Distribution')

plt.legend()
plt.show()