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 ?
In the case of the market price development of many liquid financial instruments we observe not Gaussian distribution of returns with a positive kurtosis which is characterized by the fat tails at the borders and the sharpness in the mid-area of the distribution.
Know the risk A change in stock price expected in 1 of 100 trading days has a similar size under a power law regime (red circles) and a Gaussian distribution (shaded circles).
In the first stage, different learning rates a, discount factors [gamma] were investigated with constant noise level (samples were drawn from a zero mean Gaussian distribution with variance 0.
Thus the results of this and the preceding sections suggest that the distribution of deviations of quasi-traditional single measurements and estimates of the daily mean wind speed from their true values is generally symmetrical and resembles a Gaussian distribution for small and reasonable deviations.
The probability of getting in n trials a deviation u = (hp) n from throwing N = np times a definite number, where n _ , is described by the well-known Gaussian distribution [P.
Divorce rates that show a nearly Gaussian distribution for the selected states (Figure 2) were classed using quantile and standard deviation methods.
Instead of assuming that the mobiles are randomly located in a rectangular area, we now assume that the x and y coordinates of the mobile locations have Gaussian distributions.
Table 5 shows, for the CLIA-regulated TDM assays, the distribution of the expected survey sample failure rates due to Gaussian distribution or to what have been called common-cause (random) errors in the analytic process.
In "The Normal Inverse Gaussian Distribution and Non-Gaussian Black-Scholes Contingent Pricing" section, we present a brief summary of results about the NIG family of distributions and the corresponding non-Gaussian financial theory.
The square root gaussian and the logarithmic gaussian distributions were produced by use of the transformations x = g * g and x = exp(g), respectively, where x is a value of a transformed distribution and g a gaussian-distributed random value with mean = 100 and SD = 25.
This correlation, established for gaussian distributions by Harris and Boyd, is not automatically valid for nongaussian distributions, however, because the proportions obtained for nongaussian distributions at particular distances between means can be quite different from those obtained for gaussian distributions at the same distances.
If the true mean is 0 so that one-half of the blank values are negative and are given the value 0, the average estimated SD corresponds to 57% of the SD of the unmodified gaussian distribution.