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
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
Pmf you can import it like
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 .
Here’s another example that counts the number of times each ...