Beta-Binomial Distribution

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The beta-binomial distribution is a discrete probability distribution that uses the beta distribution as a prior distribution for the probability of success in a binomial experiment. While the binomial distribution has fixed probabilities, the beta-binomial probabilities can vary from trial to trial, which makes it a more flexible distribution than the binomial.

This simple Bayesian model has been used for decades to make informed predictions in fields such as cognitive science, epidemiology, intelligence testing and marketing.

Beta-binomial distribution

Beta binomial distribution probability density function for several values of α and β [1].

The probability mass function (PMF) for the beta-binomial distribution is:

Where x ∈ { 0, …, n }.

Two shape parameters α > 0 and β > 0 define the probability of success.

  • For large values of α and β the distribution approaches a binomial distribution. In other words, the binomial distribution is the limiting distribution.
  • When α and β are both equal to 1, the distribution is a discrete uniform distribution from 0 to n. This is because when the beta function has α = 1 and β = 1, it simply equals 1, meaning that probability of getting any number of successes is equal.
  • When n = 1, the distribution is equal to a Bernoulli distribution (which models a single trial) with p chosen from a beta distribution, which has mean α/(α + β).

The cumulative distribution function (CDF) is

cdf beta-binomial distribution

where 3F2(a;b;x) is the generalized hypergeometric function 3F2(1, –x, n x + β; n x + 1, 1 – x α; 1).

The mean of the beta-binomial distribution is / (α + β).

The variance is the product of two terms [2]:

beta binomial variance
  • The first term, nαβ / (α + β)² , is the variance for a binomial distribution with the same expected value as the beta-binomial distribution.
  • The second term, (α + β + n) / (α + β + 1), is a multiplier greater than 1 for n > 1.

This means that a beta-binomial distribution with n > 1 always has a larger variance than a binomial distribution with the same expected value and number of trials.

Deriving the beta-binomial distribution formula

The beta-binomial(n, α, β) distribution is generated by choosing probability p for a binomial(n, p) distribution from a beta(α, β) distribution. You can think of it as a combination of both the binomial and beta distributions.

Let’s say you have m items on an test, and each item is tested n times. The binomial distribution PMF is:

  • P = binomial probability,
  • xi = total number of “successes” (pass or fail, heads or tails etc.) for the ith trial,
  • pi = probability of a success on an individual trial,
  • n = number of trials.

You can also think of p as being randomly drawn from a beta distribution. To create the beta-binomial formula, combine the binomial distribution PMF with the PMF for the beta distribution

to get a joint PMF:

Which can also be written (using Beta distribution properties) as:

Difference between the binomial and beta-binomial distribution

The major difference between a binomial distribution and beta-binomial distribution is that in a binomial distribution, p is fixed for a set number of trials; in a beta-binomial, p is not fixed and changes from trial to trial. One benefit is that the beta-binomial distribution can be used to model data that is overdispersed, which means that the variance is greater than the mean. This can happen in a many situations, such as when there are more extreme values in a dataset than would be expected if the data followed a normal distribution.


[1] Nschuma, CC BY-SA 3.0, via Wikimedia Commons

[2] Beta-binomial (n, α, β) distribution. Retrieved July 14, 2023 from:

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