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Gaussian function

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Gaussian curves parametrised for statistics
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Gaussian curves parametrised for statistics

A Gaussian function (named after Carl Friedrich Gauss) is a function of the form:

[f(x) = a e^]
for some real constants a > 0, b, and c.

Gaussian functions with c2 = 2 are eigenfunctions of the Fourier transform. This means that the Fourier transform of a Gaussian function is not only another Gaussian function but a scalar multiple of the function whose Fourier transform was taken.

Gaussian functions are among those functions that are elementary but lack elementary antiderivatives. Nonetheless their improper integrals over the whole real line can be evaluated exactly:

[\int_^\infty e^\,dx=\sqrt.]
This calculation can be performed by the residue theorem of complex analysis, but there is also a simple and instructive way to do the calculation. Call the value of this integral I. Then,

[I^2 = \int_^\infty e^\,dx \int_^\infty e^\,dy = \int_^\infty\int_^\infty e^\,dx\,dy.]
Note the renaming of the variable of integration from x to y (see dummy variable). We now change to plane polar coordinates

[I^2 = \int_0^\int_0^\infty e^r\,dr\,d\theta = 2\pi\int_0^\infty e^r\,dr=\pi\int_0^\infty e^\,du=\pi.]
(The substitution u = r2, du = 2r dr was used.)

Applications

The antiderivative of the Gaussian function is the error function.

Gaussian functions appear in many contexts in the natural sciences, the social sciences, mathematics, and engineering. Some examples include:

See also

Lorentzian function

 


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