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Gaussian functions are widely used in statistics to describe the normal distributions, in signal processing to define Gaussian filters, in image processing where two-dimensional Gaussians are used for Gaussian blurs, and in mathematics to solve heat equations and diffusion equations and to define the Weierstrass transform.
In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable.The general form of its probability density function is [2] [3] = ().
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.
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For example, the multivariate skewness test is not consistent against symmetric non-normal alternatives. [ 39 ] The BHEP test [ 40 ] computes the norm of the difference between the empirical characteristic function and the theoretical characteristic function of the normal distribution.
An example found by Marcus and Shepp [18]: 387 is a random lacunary Fourier series = = ( + ), where ,,,, … are independent random variables with standard normal distribution; frequencies < < < … are a fast growing sequence; and coefficients > satisfy <.
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The characteristic function + = ((+)) of the sum of two independent random variables X and Y is just the product of the two separate characteristic functions: = (), = ()