Gauss–Hermite quadrature

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Weights versus xi for four choices of n

In numerical analysis, Gauss–Hermite quadrature is a form of Gaussian quadrature for approximating the value of integrals of the following kind:

\int_{-\infty}^{+\infty} e^{-x^2} f(x)\,dx.

In this case

\int_{-\infty}^{+\infty} e^{-x^2} f(x)\,dx \approx \sum_{i=1}^n w_i f(x_i)

where n is the number of sample points used. The xi are the roots of the (physicists' version of the) Hermite polynomial Hn(x) (i = 1,2,...,n), and the associated weights wi are given by [1]

w_i = \frac {2^{n-1} n! \sqrt{\pi}} {n^2[H_{n-1}(x_i)]^2}.

Example with change of variable[edit]

Let's take a function h which variable y is Normally distributed \mathcal{N}(\mu,\sigma^2). The expectation of h corresponds to the following integral:

E[h(y)] = \int_{-\infty}^{+\infty} \frac{1}{\sigma \sqrt{2\pi}} \exp \left( -\frac{(y-\mu)^2}{2\sigma^2} \right) h(y) dy

As this doesn't exactly correspond to the Hermite polynomial, we need a change of variable:

x = \frac{y-\mu}{\sqrt{2} \sigma} \Leftrightarrow y = \sqrt{2} \sigma x + \mu

Coupled with the integration by substitution, we obtain:

E[h(y)] = \int_{-\infty}^{+\infty} \frac{1}{\sqrt{\pi}} \exp(-x^2) h(\sqrt{2} \sigma x + \mu) dx

leading to:

E[h(y)] \approx \frac{1}{\sqrt{\pi}} \sum_{i=1}^n w_i h(\sqrt{2} \sigma x_i + \mu)


  1. ^ Abramowitz, M & Stegun, I A, Handbook of Mathematical Functions, 10th printing with corrections (1972), Dover, ISBN 978-0-486-61272-0. Equation 25.4.46.

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