The Bernoulli distribution#
We are now going to generalize the coin toss experiment. Imagine an experiment with two outcomes \(0\) or \(1\). You can think of \(0\) as “failure” and \(1\) as “success”. Now assume that the experiment is successful with probability \(\theta\), which is some number between \(0\) and \(1\). For example, for a fair coin \(\theta=0.5\). But here we allow for any value. The result of such an experiment is captured by the following random variable:
\[\begin{split}
X = \begin{cases}
1,\;\text{with probability}\;\theta,\\
0,\;\text{otherwise}.
\end{cases}
\end{split}\]
In terms of the probability mass function, we have:
\[
p(X=0) = \theta,
\]
and
\[
p(X=1) = 1 - \theta.
\]
This random variable has a special name. It is called a Bernoulli random variable, named after Jacob Bernoulli. Another say that \(X\) is a Bernoulli random variable is to write:
\[
X\sim \text{Bernoulli}(\theta).
\]
which is read as:
The random variable \(X\) follows a Bernoulli distribution with parameter \(\theta\).
This is enough information to figure out what the probability mass function is. For example, with a Google search “Bernoulli distribution” you can get here.
Show code cell source
MAKE_BOOK_FIGURES=False
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib_inline
matplotlib_inline.backend_inline.set_matplotlib_formats('svg')
import seaborn as sns
sns.set_context("paper")
sns.set_style("ticks")
def set_book_style():
plt.style.use('seaborn-v0_8-white')
sns.set_style("ticks")
sns.set_palette("deep")
mpl.rcParams.update({
# Font settings
'font.family': 'serif', # For academic publishing
'font.size': 8, # As requested, 10pt font
'axes.labelsize': 8,
'axes.titlesize': 8,
'xtick.labelsize': 7, # Slightly smaller for better readability
'ytick.labelsize': 7,
'legend.fontsize': 7,
# Line and marker settings for consistency
'axes.linewidth': 0.5,
'grid.linewidth': 0.5,
'lines.linewidth': 1.0,
'lines.markersize': 4,
# Layout to prevent clipped labels
'figure.constrained_layout.use': True,
# Default DPI (will override when saving)
'figure.dpi': 600,
'savefig.dpi': 600,
# Despine - remove top and right spines
'axes.spines.top': False,
'axes.spines.right': False,
# Remove legend frame
'legend.frameon': False,
# Additional trim settings
'figure.autolayout': True, # Alternative to constrained_layout
'savefig.bbox': 'tight', # Trim when saving
'savefig.pad_inches': 0.1 # Small padding to ensure nothing gets cut off
})
def save_for_book(fig, filename, is_vector=True, **kwargs):
"""
Save a figure with book-optimized settings.
Parameters:
-----------
fig : matplotlib figure
The figure to save
filename : str
Filename without extension
is_vector : bool
If True, saves as vector at 1000 dpi. If False, saves as raster at 600 dpi.
**kwargs : dict
Additional kwargs to pass to savefig
"""
# Set appropriate DPI and format based on figure type
if is_vector:
dpi = 1000
ext = '.pdf'
else:
dpi = 600
ext = '.tif'
# Save the figure with book settings
fig.savefig(f"{filename}{ext}", dpi=dpi, **kwargs)
def make_full_width_fig():
return plt.subplots(figsize=(4.7, 2.9), constrained_layout=True)
def make_half_width_fig():
return plt.subplots(figsize=(2.35, 1.45), constrained_layout=True)
if MAKE_BOOK_FIGURES:
set_book_style()
make_full_width_fig = make_full_width_fig if MAKE_BOOK_FIGURES else lambda: plt.subplots()
make_half_width_fig = make_half_width_fig if MAKE_BOOK_FIGURES else lambda: plt.subplots()
import numpy as np
Let’s use the functionality of scipy.stats to define a Bernoulli random variable and sample from it:
import scipy.stats as st
I will pick \(\theta=0.6\):
theta = 0.6
X = st.bernoulli(theta)
This objects encapsulates everything related to the random variable \(X\).
For example, X.support() tells you which values it takes:
X.support()
(0, 1)
To evaluate the probability mass function you can use:
X.pmf(0)
0.3999999999999999
X.pmf(1)
0.6
Finally, you can use X.rvs() to generate samples from this random variable.
for i in range(10):
print(X.rvs())
0
1
1
1
1
1
0
1
0
1
If you want to take many samples at once, here is how:
X.rvs(1000)
Show code cell output
array([0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1,
0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1,
0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0,
0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1,
1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,
0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1,
0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0,
0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1,
1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0,
1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1,
1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0,
1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1,
0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0,
0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0,
1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1,
1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1,
0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0,
1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1,
0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0,
0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0,
1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1,
0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1,
0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1,
1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,
1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1,
1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,
0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,
1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1,
1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0,
1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,
1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0,
1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1,
1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1,
1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1,
1, 1, 0, 0, 1, 1, 0, 1, 0, 1])
Let’s now take many samples and do their histogram:
fig, ax = make_full_width_fig()
xs = X.rvs(size=1000)
ax.hist(xs)
ax.set_xlabel('$x$')
ax.set_ylabel('Counts')
save_for_book(fig, 'ch9.fig1')
Questions#
Rerun the code above with \(\theta=0.8\).