Think Bayes

This notebook presents example code and exercise solutions for Think Bayes.

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 classes from thinkbayes2
from thinkbayes2 import Hist, Pmf, Suite, Beta
import thinkplot

Unreliable observation

Suppose that instead of observing coin tosses directly, you measure the outcome using an instrument that is not always correct. Specifically, suppose there is a probability y that an actual heads is reported as tails, or actual tails reported as heads.

Write a class that estimates the bias of a coin given a series of outcomes and the value of y.

How does the spread of the posterior distribution depend on y?



In [2]:

    
# Solution

# Here's a class that models an unreliable coin

class UnreliableCoin(Suite):
    
    def __init__(self, prior, y):
        """
        prior: seq or map
        y: probability of accurate measurement
        """
        super().__init__(prior)
        self.y = y
    
    def Likelihood(self, data, hypo):
        """
        data: outcome of unreliable measurement, either 'H' or 'T'
        hypo: probability of heads, 0-100
        """
        x = hypo / 100
        y = self.y
        if data == 'H':
            return x*y + (1-x)*(1-y)
        else:
            return x*(1-y) + (1-x)*y



In [3]:

    
# Solution

# Now let's initialize an UnreliableCoin with `y=0.9`:

prior = range(0, 101)
suite = UnreliableCoin(prior, y=0.9)
thinkplot.Pdf(suite)



In [4]:

    
# Solution

# And update with 3 heads and 7 tails.

for outcome in 'HHHTTTTTTT':
    suite.Update(outcome)
    
thinkplot.Pdf(suite)



In [5]:

    
# Solution

# Now let's try it out with different values of `y`:

def plot_posterior(y, data):
    prior = range(0, 101)
    suite = UnreliableCoin(prior, y=y)
    for outcome in data:
        suite.Update(outcome)
    
    thinkplot.Pdf(suite, label='y=%g' % y)



In [6]:

    
# Solution

# The posterior distribution gets wider as the measurement gets less reliable.

data = 'HHHTTTTTTT'
plot_posterior(1, data)
plot_posterior(0.8, data)
plot_posterior(0.6, data)
thinkplot.decorate(xlabel='Probability of heads (x)',
                   ylabel='PMF')



In [7]:

    
# Solution

# At `y=0.5`, the measurement provides no information, so the posterior equals the prior:

plot_posterior(0.5, data)
thinkplot.decorate(xlabel='Probability of heads (x)',
                   ylabel='PMF')



In [8]:

    
# Solution

# As the coin gets less reliable (below `y=0.5`) the distribution gets narrower again.  
# In fact, a measurement with `y=0` is just as good as one with `y=1`, 
# provided that we know what `y` is.

plot_posterior(0.4, data)
plot_posterior(0.2, data)
plot_posterior(0.0, data)
thinkplot.decorate(xlabel='Probability of heads (x)',
                   ylabel='PMF')



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