# Orthogonal polynomials and the beta distribution

This post shows a connection between three families of orthogonal polynomials—Legendre, Chebyshev, and Jacobi—and the beta distribution.

## Legendre, Chebyshev, and Jacobi polynomials

A family of polynomials Pk is orthogonal over the interval [−1, 1] with respect to a weight w(x) if

whenever mn.

If w(x) = 1, we get the Legendre polynomials.

If w(x) = (1 − x²)−1/2 we get the Chebyshev polynomials.

These are both special cases of the Jacobi polynomials which have weight w(x) = (1 − x)α (1 + x)β. Legendre polynomials correspond to α = β = 0, and Chebyshev polynomials correspond to α = β = −1/2.

## Connection to beta distribution

The weight function for Jacobi polynomials is a rescaling of the density function of a beta distribution. The change of variables x = 1 − 2u shows

The right side is proportional to the expected value of f(1 − 2X) where X is a random variable with a beta(α + 1, β + 1) distribution. So for fixed α and β, if mn and X has a beta(α + 1, β+1) distribution, then the expected value of Pm(1 − 2X) Pn(1 − 2X) is zero.

While we’re at it, we’ll briefly mention two other connections between orthogonal polynomials and probability: Laguerre polynomials and Hermite polynomials.

## Laguerre polynomials

The Laguerre polynomials are orthogonal over the interval [0, ∞) with weight w(x) = xα exp(−x), which is proportional to the density of a gamma random variable with shape α + 1 and scale 1.

## Hermite polynomials

There are two minor variations on the Hermite polynomials, depending on whether you take the weight to be exp(−x²) or exp(−x²/2). These are sometimes known as the physicist’s Hermite polynomials and the probabilist’s Hermite polynomials. Naturally we’re interested in the latter. The probabilist’s Hermite polynomials are orthogonal over (−∞, ∞) with the standard normal (Gaussian) density as the weight.

## 4 thoughts on “Orthogonal polynomials and the beta distribution”

1. Pseudonym

Are we going to talk about the connection to Gaussian quadrature next?

2. Probably not, but I did post notes on that here.

3. Andy Grieve

I have often used orthogonal polynomials to determine integrals in a Bayesian context. I used Harper polynomials, orthogonal to t-distributions, in my thesis.

But the first time I used them were Jacobi polynomials to approximate the sampling distribution of the Greenhouse-Geisser correction factor in repeated measures designs.

4. Cheysvev polys have beta equal to zero?