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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.
The probability density of the standard Gaussian distribution (standard normal distribution, with zero mean and unit variance) is often denoted with the Greek letter . [10] The alternative form of the Greek letter phi, , is also used quite often.
Gaussian measures with mean = are known as centered Gaussian measures. The Dirac measure δ μ {\displaystyle \delta _{\mu }} is the weak limit of γ μ , σ 2 n {\displaystyle \gamma _{\mu ,\sigma ^{2}}^{n}} as σ → 0 {\displaystyle \sigma \to 0} , and is considered to be a degenerate Gaussian measure ; in contrast, Gaussian measures with ...
The range is a specific example of order statistics. In particular, the range is a linear function of order statistics, which brings it into the scope of L-estimation . See also
The probability density function for the random matrix X (n × p) that follows the matrix normal distribution , (,,) has the form: (,,) = ([() ()]) / | | / | | /where denotes trace and M is n × p, U is n × n and V is p × p, and the density is understood as the probability density function with respect to the standard Lebesgue measure in , i.e.: the measure corresponding to integration ...
This means that the sum of two independent normally distributed random variables is normal, with its mean being the sum of the two means, and its variance being the sum of the two variances (i.e., the square of the standard deviation is the sum of the squares of the standard deviations). [1]
The generalized normal distribution (GND) or generalized Gaussian distribution (GGD) is either of two families of parametric continuous probability distributions on the real line. Both families add a shape parameter to the normal distribution. To distinguish the two families, they are referred to below as "symmetric" and "asymmetric"; however ...
Also, the characteristic function of the sample mean X of n independent observations has characteristic function φ X (t) = (e −|t|/n) n = e −|t|, using the result from the previous section. This is the characteristic function of the standard Cauchy distribution: thus, the sample mean has the same distribution as the population itself.