Gaussian distribution


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Gaussian distribution

[¦gau̇·sē·ən ‚dis·trə′byü·shən]
(statistics)

Gaussian distribution

Gaussian distribution

A random distribution of events that is graphed as the famous "bell-shaped curve." It is used to represent a normal or statistically probable outcome and shows most samples falling closer to the mean value. See Gaussian noise and Gaussian blur.
References in periodicals archive ?
The probability distribution is found for the link distance between two randomly positioned mobile radios in a wireless network for two representative deployment scenarios: (1) the mobile locations are uniformly distributed over a rectangular area and (2) the x and y coordinates of the mobile locations have Gaussian distributions.
The Gaussian distribution obtained by convolution has [[mu].
Therefore, the posterior density function of the observation y is a multivariate Gaussian distribution with mean E[y] = A[mu] and covariance Cov[y] = A[SIGMA][A.
j] is the weight of the j -- th Gaussian distribution with [[summation].
Note that the Gaussian copula assumption is only for connecting the CDFs of the random variables, which is less restrictive than assuming a joint Gaussian distribution for the variables themselves.
1,Z], corresponding to a high-variance cluster in Z, the marks come from a mixture Gaussian distribution with equal means but different variances:
We divide our experiments into two groups with the increasing sample complexity: p = (1/2)N, N random samples from the multivariate Gaussian distribution N(0, [[sigma].
Minimum distance is stated as difference of Gaussian distributions.
The gaussian distribution ranges from minus infinity to plus infinity, so as long as the SD is not zero, it is a mathematical certainty that the probability of results larger than 15.
The Gaussian distribution is analytically tractable and has a long history as a model of stock returns.
The Gaussian distribution is one of the simplest filters that can be applied to an image in order to blur it, or smooth it out.
After a short review on signal detection under Gaussian noise in [section]2, in [section]3 we incorporate small deviation from Gaussian distribution in terms of the Edgeworth expansion and calculate the likelihood ratio with it.

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