21.2. The Beta-Binomial Distribution

As in the previous section, let \(X\) have the beta \((r, s)\) prior, and given \(X = p\) let the \(S_n\) be the number of heads in the first \(n\) tosses of a \(p\)-coin.

All the calculations we carried out in the previous section were under the condition that \(S_n = k\), but we never needed to find the probability of this event. It was part of the constant that made the posterior density of \(X\) integrate to 1.

We can now find \(P(S_n = k)\) by writing the posterior density in two ways:

  • By recalling that it is the beta \((r+k, s+n-k)\) density:

\[ f_{X \vert S_n=k} (p) ~ = ~ C(r+k, s+n-k)p^{r+k-1}(1-p)^{s+n-k-1}, ~~~~ 0 < p < 1 \]
  • By using Bayes’ Rule:

\[ f_{X \vert S_n=k} (p) ~ = ~ \frac{C(r, s) p^{r-1}(1-p)^{s-1} \cdot \binom{n}{k} p^k (1-p)^{n-k}}{P(S_n = k)}, ~~~~ 0 < p < 1 \]

Now equate constants:

\[ \frac{C(r, s) \binom{n}{k}}{P(S_n = k)} ~ = ~ C(r+k, s+n-k) \]

21.2.1. Beta-Binomial Probabilities

So for \(k\) in the range 0 through \(n\),

\[ P(S_n = k) ~ = ~ \binom{n}{k} \frac{C(r, s)}{C(r+k, s+n-k)} \]

where \(C(r,s)\) is the constant in the beta \((r, s)\) density, given by

\[ C(r, s) ~ = ~ \frac{\Gamma(r+s)}{\Gamma(r)\Gamma(s)} \]

This discrete distribution is called the beta-binomial distribution with parameters \(r\), \(s\), and \(n\). It is the distribution of the number of heads in \(n\) tosses of a coin that lands heads with a probability picked according to the beta \((r, s)\) distribution.

One \((r, s)\) pair is particularly interesting: \(r = s = 1\). That’s the case when \(X\) has the uniform prior. The distribution of \(S_n\) reduces to

\[ P(S_n = k ) ~ = ~ \frac{n!}{k!(n-k)!} \cdot \frac{1!}{0!0!} \cdot \frac{k!(n-k)!}{(n+1)!} ~ = ~ \frac{1}{n+1} \]

There’s no \(k\) in the answer! The conclusion is that if you choose \(p\) uniformly between 0 and 1 and toss a \(p\)-coin \(n\) times, the distribution of the number of heads is uniform on \(\{ 0, 1, 2, \ldots, n\}\).

If you choose \(p\) uniformly between 0 and 1, then for the conditional distribution of \(S_n\) given that \(p\) was the selected value is binomial \((n, p)\). But the unconditional distribution of \(S_n\) is uniform.

21.2.2. Checking by Integration

If you prefer, you can find the distribution of \(S_n\) directly, by conditioning on \(X\). \($ \begin{align*} P(S_n = k) ~ &= \int_0^1 P(S_n = k \mid X = p)f_X(p)dp \\ \\ &= ~ \int_0^1 \binom{n}{k} p^k(1-p)^{n-k}C(r, s)p^{r-1}(1-p)^{s-1}dp \\ \\ &= ~ \binom{n}{k} C(r, s) \int_0^1 p^{r+k-1}(1-p)^{s+n-k-1} dp \\ \\ &= ~ \binom{n}{k} C(r, s) \frac{1}{C(r+k, s+n-k)} \end{align*} $\)

21.2.3. Expectation

Given \(X = p\), the conditional distribution of \(S_n\) is binomial \((n, p)\). Therefore

\[ E(S_n \mid X = p) ~ = ~ np \]

or, equivalently,

\[ E(S_n \mid X) ~ = ~ nX \]

By iteration,

\[ E(S_n) ~ = ~ E(nX) ~ = ~ nE(X) ~ = ~ n\frac{r}{r+s} \]

The expected proportion of heads in \(n\) tosses is

\[ E\big{(} \frac{S_n}{n} \big{)} ~ = ~ \frac{r}{r+s} \]

which is the expectation of the prior distribution of \(X\).

In the next section we will examine the long run behavior of this random proportion.

21.2.4. Endnote

The unconditional probability \(P(S_n = k)\) appeared in the denominator of our calculation of the posterior density of \(X\) given \(S_n\). Because of the simplifications that result from using conjugate priors, we were able to calculate the denominator in a couple of different ways. But often the calculation can be intractable, especially in high dimensional settings. Methods of dealing with this problem are covered in more advanced courses.