Chapter 2  Computational Statistics

2.1  Distributions

In statistics a distribution is a set of values and their corresponding probabilities.

For example, if you roll a six-sided die, the set of possible values is the numbers 1 to 6, and the probability associated with each value is 1/6.

As another example, you might be interested in how many times each word appears in common English usage. You could build a distribution that includes each word and how many times it appears.

To represent a distribution in Python, you could use a dictionary that maps from each value to its probability. I have written a class called Pmf that uses a Python dictionary in exactly that way, and provides a number of useful methods. I called the class Pmf in reference to a probability mass function, which is a way to represent a distribution mathematically.

Pmf is defined in a Python module I wrote to accompany this book, thinkbayes.py. You can download it from http://thinkbayes.com/thinkbayes.py. For more information see Section 0.3.

To use Pmf you can import it like this:

from thinkbayes import Pmf

The following code builds a Pmf to represent the distribution of outcomes for a six-sided die:

pmf = Pmf()
for x in [1,2,3,4,5,6]:
    pmf.Set(x, 1/6.0)

Pmf creates an empty Pmf with no values. The Set method sets the probability associated with each value to 1/6.

Here’s another example that counts the number of times each word appears in a sequence:

pmf = Pmf()
for word in word_list:
    pmf.Incr(word, 1)

Incr increases the “probability” associated with each word by 1. If a word is not already in the Pmf, it is added.

I put “probability” in quotes because in this example, the probabilities are not normalized; that is, they do not add up to 1. So they are not true probabilities.

But in this example the word counts are proportional to the probabilities. So after we count all the words, we can compute probabilities by dividing through by the total number of words. Pmf provides a method, Normalize, that does exactly that:

pmf.Normalize()

Once you have a Pmf object, you can ask for the probability associated with any value:

print pmf.Prob('the')

And that would print the frequency of the word “the” as a fraction of the words in the list.

Pmf uses a Python dictionary to store the values and their probabilities, so the values in the Pmf can be any hashable type. The probabilities can be any numerical type, but they are usually floating-point numbers (type float).

2.2  The cookie problem

In the context of Bayes’s theorem, it is natural to use a Pmf to map from each hypothesis to its probability. In the cookie problem, the hypotheses are B₁ and B₂. In Python, I represent them with strings:

pmf = Pmf()
pmf.Set('Bowl 1', 0.5)
pmf.Set('Bowl 2', 0.5)

This distribution, which contains the priors for each hypothesis, is called (wait for it) the prior distribution.

To update the distribution based on new data (the vanilla cookie), we multiply each prior by the corresponding likelihood. The likelihood of drawing a vanilla cookie from Bowl 1 is 3/4. The likelihood for Bowl 2 is 1/2.

pmf.Mult('Bowl 1', 0.75)
pmf.Mult('Bowl 2', 0.5)

Mult does what you would expect. It gets the probability for the given hypothesis and multiplies by the given likelihood.

After this update, the distribution is no longer normalized, but because these hypotheses are mutually exclusive and collectively exhaustive, we can renormalize:

pmf.Normalize()

The result is a distribution that contains the posterior probability for each hypothesis, which is called (wait now) the posterior distribution.

Finally, we can get the posterior probability for Bowl 1:

print pmf.Prob('Bowl 1')

And the answer is 0.6. You can download this example from http://thinkbayes.com/cookie.py. For more information see Section 0.3.

2.3  The Bayesian framework

Before we go on to other problems, I want to rewrite the code from the previous section to make it more general. First I’ll define a class to encapsulate the code related to this problem:

class Cookie(Pmf):

    def __init__(self, hypos):
        Pmf.__init__(self)
        for hypo in hypos:
            self.Set(hypo, 1)
        self.Normalize()

A Cookie object is a Pmf that maps from hypotheses to their probabilities. The __init__ method gives each hypothesis the same prior probability. As in the previous section, there are two hypotheses:

    hypos = ['Bowl 1', 'Bowl 2']
    pmf = Cookie(hypos)

Cookie provides an Update method that takes data as a parameter and updates the probabilities:

    def Update(self, data):
        for hypo in self.Values():
            like = self.Likelihood(data, hypo)
            self.Mult(hypo, like)
        self.Normalize()

Update loops through each hypothesis in the suite and multiplies its probability by the likelihood of the data under the hypothesis, which is computed by Likelihood:

    mixes = {
        'Bowl 1':dict(vanilla=0.75, chocolate=0.25),
        'Bowl 2':dict(vanilla=0.5, chocolate=0.5),
        }

    def Likelihood(self, data, hypo):
        mix = self.mixes[hypo]
        like = mix[data]
        return like

Likelihood uses mixes, which is a dictionary that maps from the name of a bowl to the mix of cookies in the bowl.

Here’s what the update looks like:

    pmf.Update('vanilla')

And then we can print the posterior probability of each hypothesis:

    for hypo, prob in pmf.Items():
        print hypo, prob

The result is

Bowl 1 0.6
Bowl 2 0.4

which is the same as what we got before. This code is more complicated than what we saw in the previous section. One advantage is that it generalizes to the case where we draw more than one cookie from the same bowl (with replacement):

    dataset = ['vanilla', 'chocolate', 'vanilla']
    for data in dataset:
        pmf.Update(data)

The other advantage is that it provides a framework for solving many similar problems. In the next section we’ll solve the Monty Hall problem computationally and then see what parts of the framework are the same.

The code in this section is available from http://thinkbayes.com/cookie2.py. For more information see Section 0.3.

2.4  The Monty Hall problem

To solve the Monty Hall problem, I’ll define a new class:

class Monty(Pmf):

    def __init__(self, hypos):
        Pmf.__init__(self)
        for hypo in hypos:
            self.Set(hypo, 1)
        self.Normalize()

So far Monty and Cookie are exactly the same. And the code that creates the Pmf is the same, too, except for the names of the hypotheses:

    hypos = 'ABC'
    pmf = Monty(hypos)

Calling Update is pretty much the same:

    data = 'B'
    pmf.Update(data)

And the implementation of Update is exactly the same:

    def Update(self, data):
        for hypo in self.Values():
            like = self.Likelihood(data, hypo)
            self.Mult(hypo, like)
        self.Normalize()

The only part that requires some work is Likelihood:

    def Likelihood(self, data, hypo):
        if hypo == data:
            return 0
        elif hypo == 'A':
            return 0.5
        else:
            return 1

Finally, printing the results is the same:

    for hypo, prob in pmf.Items():
        print hypo, prob

And the answer is

A 0.333333333333
B 0.0
C 0.666666666667

In this example, writing Likelihood is a little complicated, but the framework of the Bayesian update is simple. The code in this section is available from http://thinkbayes.com/monty.py. For more information see Section 0.3.

2.5  Encapsulating the framework

Now that we see what elements of the framework are the same, we can encapsulate them in an object—a Suite is a Pmf that provides __init__, Update, and Print:

class Suite(Pmf):
    """Represents a suite of hypotheses and their probabilities."""

    def __init__(self, hypo=tuple()):
        """Initializes the distribution."""

    def Update(self, data):
        """Updates each hypothesis based on the data."""

    def Print(self):
        """Prints the hypotheses and their probabilities."""

The implementation of Suite is in thinkbayes.py. To use Suite, you should write a class that inherits from it and provides Likelihood. For example, here is the solution to the Monty Hall problem rewritten to use Suite:

from thinkbayes import Suite

class Monty(Suite):

    def Likelihood(self, data, hypo):
        if hypo == data:
            return 0
        elif hypo == 'A':
            return 0.5
        else:
            return 1

And here’s the code that uses this class:

    suite = Monty('ABC')
    suite.Update('B')
    suite.Print()

You can download this example from http://thinkbayes.com/monty2.py. For more information see Section 0.3.

2.6  The M&M problem

We can use the Suite framework to solve the M&M problem. Writing the Likelihood function is tricky, but everything else is straightforward.

First I need to encode the color mixes from before and after 1995:

    mix94 = dict(brown=30,
                 yellow=20,
                 red=20,
                 green=10,
                 orange=10,
                 tan=10)

    mix96 = dict(blue=24,
                 green=20,
                 orange=16,
                 yellow=14,
                 red=13,
                 brown=13)

Then I have to encode the hypotheses:

    hypoA = dict(bag1=mix94, bag2=mix96)
    hypoB = dict(bag1=mix96, bag2=mix94)

hypoA represents the hypothesis that Bag 1 is from 1994 and Bag 2 from 1996. hypoB is the other way around.

Next I map from the name of the hypothesis to the representation:

    hypotheses = dict(A=hypoA, B=hypoB)

And finally I can write Likelihood. In this case the hypothesis, hypo, is a string, either A or B. The data is a tuple that specifies a bag and a color.

    def Likelihood(self, data, hypo):
        bag, color = data
        mix = self.hypotheses[hypo][bag]
        like = mix[color]
        return like

Here’s the code that creates the suite and updates it:

    suite = M_and_M('AB')

    suite.Update(('bag1', 'yellow'))
    suite.Update(('bag2', 'green'))

    suite.Print()

And here’s the result:

A 0.740740740741
B 0.259259259259

The posterior probability of A is approximately 20/27, which is what we got before.

The code in this section is available from http://thinkbayes.com/m_and_m.py. For more information see Section 0.3.

2.7  Discussion

This chapter presents the Suite class, which encapsulates the Bayesian update framework.

Suite is an abstract type, which means that it defines the interface a Suite is supposed to have, but does not provide a complete implementation. The Suite interface includes Update and Likelihood, but the Suite class only provides an implementation of Update, not Likelihood.

A concrete type is a class that extends an abstract parent class and provides an implementation of the missing methods. For example, Monty extends Suite, so it inherits Update and provides Likelihood.

If you are familiar with design patterns, you might recognize this as an example of the template method pattern. You can read about this pattern at http://en.wikipedia.org/wiki/Template_method_pattern.

Most of the examples in the following chapters follow the same pattern; for each problem we define a new class that extends Suite, inherits Update, and provides Likelihood. In a few cases we override Update, usually to improve performance.

2.8  Exercises

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