This is one of the 100 recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python.

# 7.3. Getting started with Bayesian methods¶

Let $q$ be the probability of obtaining a head. Whereas $q$ was just a fixed number in the previous recipe, we consider here that it is a random variable. Initially, this variable follows a distribution called the prior distribution. It represents our knowledge about $q$ before we start flipping the coin. We will update this distribution after each trial (posterior distribution).

1. First, we assume that $q$ is a uniform random variable on the interval $[0, 1]$. That's our prior distribution: for all $q$, $P(q)=1$.
2. Then, we flip our coin $n$ times. We note $x_i$ the outcome of the $i$-th flip ($0$ for tail, $1$ for head).
3. What is the probability distribution of $q$ knowing the observations $x_i$? Bayes' formula allows us to compute the posterior distribution analytically (see the next section for the mathematical details):
$$P(q | \{x_i\}) = \frac{P(\{x_i\} | q) P(q)}{\displaystyle\int_0^1 P(\{x_i\} | q) P(q) dq} = (n+1)\binom n h q^h (1-q)^{n-h}$$

We define the posterior distribution according to the mathematical formula above. We remark this this expression is $(n+1)$ times the probability mass function (PMF) of the binomial distribution, which is directly available in scipy.stats. (http://en.wikipedia.org/wiki/Binomial_distribution)

In [ ]:
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
%matplotlib inline

In [ ]:
posterior = lambda n, h, q: (n+1) * st.binom(n, q).pmf(h)


Let's plot this distribution for an observation of $h=61$ heads and $n=100$ total flips.

In [ ]:
n = 100
h = 61
q = np.linspace(0., 1., 1000)
d = posterior(n, h, q)

In [ ]:
plt.figure(figsize=(5,3));
plt.plot(q, d, '-k');
plt.xlabel('q parameter');
plt.ylabel('Posterior distribution');
plt.ylim(0, d.max()+1);

1. This distribution indicates the plausible values for $q$ given the observations. We could use it to derive a credible interval, likely to contain the actual value. (http://en.wikipedia.org/wiki/Credible_interval)

We can also derive a point estimate. For example, the maximum a posteriori (MAP) estimation consists in considering the maximum of this distribution as an estimate for $q$. We can find this maximum analytically or numerically. Here, we find analytically $\hat q = h/n$, which looks quite sensible. (http://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation)

You'll find all the explanations, figures, references, and much more in the book (to be released later this summer).

IPython Cookbook, by Cyrille Rossant, Packt Publishing, 2014 (500 pages).