This notebook presents code and exercises from Think Bayes, second edition.

Copyright 2018 Allen B. Downey

MIT License: https://opensource.org/licenses/MIT

```
In [1]:
```# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
import numpy as np
import pandas as pd
from thinkbayes2 import Pmf, Cdf, Suite, Joint
import thinkplot

For adult male residents of the US, the mean and standard deviation of height are 178 cm and 7.7 cm. For adult female residents the corresponding stats are 163 cm and 7.3 cm. Suppose you learn that someone is 170 cm tall. What is the probability that they are male?

Run this analysis again for a range of observed heights and plot a curve that shows P(male) versus height. What is the mathematical form of this function?

`norm`

from `scipy.stats`

, which returns a "frozen" random variable (RV) that represents a normal distribution with given parameters.

```
In [2]:
```from scipy.stats import norm
dist_height = dict(male=norm(178, 7.7),
female=norm(163, 7.3))

```
Out[2]:
```

Write a class that implements `Likelihood`

using the frozen distributions. Here's starter code:

```
In [3]:
```class Height(Suite):
def Likelihood(self, data, hypo):
"""
data: height in cm
hypo: 'male' or 'female'
"""
return 1

```
In [4]:
```# Solution
class Height(Suite):
def Likelihood(self, data, hypo):
"""
data: height in cm
hypo: 'male' or 'female'
"""
height = data
return dist_height[hypo].pdf(height)

Here's the prior.

```
In [5]:
```suite = Height(dict(male=0.49, female=0.51))
for hypo, prob in suite.Items():
print(hypo, prob)

```
```

And the update:

```
In [6]:
```suite.Update(170)
for hypo, prob in suite.Items():
print(hypo, prob)

```
```

```
In [7]:
```# Solution
def prob_male(height):
suite = Height(dict(male=0.49, female=0.51))
suite.Update(height)
return suite['male']

```
In [8]:
```# Solution
heights = np.linspace(130, 210)
series = pd.Series(index=heights)
for height in heights:
series[height] = prob_male(height)

```
In [9]:
```# Solution
thinkplot.plot(series)
thinkplot.decorate(xlabel='Height (cm)',
ylabel='Probability of being male')

```
```

Suppose I choose two residents of the U.S. at random. A is taller than B. How tall is A?

What if I tell you that A is taller than B by more than 5 cm. How tall is A?

For adult male residents of the US, the mean and standard deviation of height are 178 cm and 7.7 cm. For adult female residents the corresponding stats are 163 cm and 7.3 cm.

Here are distributions that represent the heights of men and women in the U.S.

```
In [10]:
```dist_height = dict(male=norm(178, 7.7),
female=norm(163, 7.3))

```
Out[10]:
```

```
In [11]:
```hs = np.linspace(130, 210)
ps = dist_height['male'].pdf(hs)
male_height_pmf = Pmf(dict(zip(hs, ps)));

```
In [12]:
```ps = dist_height['female'].pdf(hs)
female_height_pmf = Pmf(dict(zip(hs, ps)));

```
In [13]:
```thinkplot.Pdf(male_height_pmf, label='Male')
thinkplot.Pdf(female_height_pmf, label='Female')
thinkplot.decorate(xlabel='Height (cm)',
ylabel='PMF',
title='Adult residents of the U.S.')

```
```

Use `thinkbayes2.MakeMixture`

to make a `Pmf`

that represents the height of all residents of the U.S.

```
In [14]:
```# Solution
from thinkbayes2 import MakeMixture
metapmf = Pmf({male_height_pmf:0.49, female_height_pmf:0.51})
mix = MakeMixture(metapmf)
mix.Mean()

```
Out[14]:
```

```
In [15]:
```# Solution
thinkplot.Pdf(mix)
thinkplot.decorate(xlabel='Height (cm)',
ylabel='PMF',
title='Adult residents of the U.S.')

```
```

```
In [16]:
```# Solution
class Heights(Suite, Joint):
def Likelihood(self, data, hypo):
"""
data: who is taller, 'A' or 'B'?
hypo: h1, h2
"""
h1, h2 = hypo
if data == 'A':
return 1 if h1 > h2 else 0
else:
return 1 if h2 > h1 else 0

Write a function that initializes your `Suite`

with an appropriate prior.

```
In [17]:
```# Solution
# We could also use MakeJoint for this
def make_prior(A, B):
suite = Heights()
for h1, p1 in A.Items():
for h2, p2 in B.Items():
suite[h1, h2] = p1 * p2
return suite

```
In [18]:
```suite = make_prior(mix, mix)
suite.Total()

```
Out[18]:
```

```
In [19]:
```thinkplot.Contour(suite)
thinkplot.decorate(xlabel='B Height (cm)',
ylabel='A Height (cm)',
title='Posterior joint distribution')

```
```

`Suite`

, then plot the joint distribution and the marginal distribution, and compute the posterior means for `A`

and `B`

.

```
In [20]:
```# Solution
suite.Update(0)

```
Out[20]:
```

```
In [21]:
```# Solution
thinkplot.Contour(suite)
thinkplot.decorate(xlabel='B Height (cm)',
ylabel='A Height (cm)',
title='Posterior joint distribution')

```
```

```
In [22]:
```# Solution
posterior_a = suite.Marginal(0)
posterior_b = suite.Marginal(1)
thinkplot.Pdf(posterior_a, label='A')
thinkplot.Pdf(posterior_b, label='B')
thinkplot.decorate(xlabel='Height (cm)',
ylabel='PMF',
title='Posterior marginal distributions')
posterior_a.Mean(), posterior_b.Mean()

```
Out[22]:
```

```
In [23]:
```# Solution
# The prior for A and B is the mixture we computed above.
A = mix
B = mix;

```
In [24]:
```# Solution
def faceoff(player1, player2, data):
"""Compute the posterior distributions for both players.
player1: Pmf
player2: Pmf
data: margin by which player1 beats player2
"""
joint = make_prior(player1, player2)
joint.Update(data)
return joint.Marginal(0), joint.Marginal(1)

```
In [25]:
```# Solution
# We can think of the scenario as a sequence of "faceoffs"
# where A wins 8 and loses 1
for i in range(8):
A, _ = faceoff(A, B, 'A')
A, B = faceoff(A, B, 'B');

```
In [26]:
```# Solution
# Here's the posterior distribution for A
thinkplot.Pdf(A)
A.Mean()

```
Out[26]:
```

```
In [27]:
```# Solution
# Now we can compute the total probability of being male,
# conditioned on the posterior distribution of height.
total = 0
for h, p in A.Items():
total += p * prob_male(h)
total

```
Out[27]:
```

```
In [28]:
```# Solution
# Here's a second solution based on an "annotated" mix that keeps
# track of M and F
annotated_mix = Suite()
for h, p in male_height_pmf.Items():
annotated_mix['M', h] = p * 0.49
for h, p in female_height_pmf.Items():
annotated_mix['F', h] = p * 0.51
annotated_mix.Total()

```
Out[28]:
```

```
In [29]:
```# Solution
# Here's an updated Heights class that can handle the
# annotated mix
class Heights2(Suite, Joint):
def Likelihood(self, data, hypo):
"""
data: who is taller, A or B
hypo: (MF1, h1), (MF2, h2)
"""
(_, h1), (_, h2) = hypo
if data == 'A':
return 1 if h1 > h2 else 0
if data == 'B':
return 1 if h2 > h1 else 0

```
In [30]:
```# Solution
# Everything else is pretty much the same
from thinkbayes2 import MakeJoint
def faceoff(player1, player2, data):
joint = Heights2(MakeJoint(player1, player2))
joint.Update(data)
return joint.Marginal(0), joint.Marginal(1)

```
In [31]:
```# Solution
A = annotated_mix
B = annotated_mix;

```
In [32]:
```# Solution
for i in range(8):
A, _ = faceoff(A, B, 'A')
A, _ = faceoff(A, B, 'B');

```
In [33]:
```# Solution
# Now the posterior distribution for A contains the
# probability of being male
A_male = Joint(A).Marginal(0)

```
Out[33]:
```

```
In [34]:
```# Solution
# The posterior distribution for A also contains the
# posterior probability of height
A_height = Joint(A).Marginal(1)
thinkplot.Pdf(A_height)
A_height.Mean()

```
Out[34]:
```

```
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