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Mathematically, the derivatives of the Gaussian function can be represented using Hermite functions. For unit variance, the n-th derivative of the Gaussian is the Gaussian function itself multiplied by the n-th Hermite polynomial, up to scale. Consequently, Gaussian functions are also associated with the vacuum state in quantum field theory.
Another way is to define the cdf () as the probability that a sample lies inside the ellipsoid determined by its Mahalanobis distance from the Gaussian, a direct generalization of the standard deviation. [13] In order to compute the values of this function, closed analytic formula exist, [13] as follows.
A different technique, which goes back to Laplace (1812), [3] is the following. Let = =. Since the limits on s as y → ±∞ depend on the sign of x, it simplifies the calculation to use the fact that e −x 2 is an even function, and, therefore, the integral over all real numbers is just twice the integral from zero to infinity.
In other words, where f is a (normalized) Gaussian function with variance σ 2 /2 π, centered at zero, and its Fourier transform is a Gaussian function with variance σ −2 /2 π. Gaussian functions are examples of Schwartz functions (see the discussion on tempered distributions below).
The moment generating function of a real random variable is the expected value of , as a function of the real parameter . For a normal distribution with density f {\displaystyle f} , mean μ {\displaystyle \mu } and variance σ 2 {\textstyle \sigma ^{2}} , the moment generating function exists and is equal to
In signal processing, multidimensional discrete convolution refers to the mathematical operation between two functions f and g on an n-dimensional lattice that produces a third function, also of n-dimensions. Multidimensional discrete convolution is the discrete analog of the multidimensional convolution of functions on Euclidean space.
Consider a function h(y), ... "A Gaussian quadrature procedure for use in the solution of the Boltzmann equation and related problems". J. Comput.
The Gaussian quadrature chooses more suitable points instead, so even a linear function approximates the function better (the black dashed line). As the integrand is the third-degree polynomial y ( x ) = 7 x 3 – 8 x 2 – 3 x + 3 , the 2-point Gaussian quadrature rule even returns an exact result.