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.
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As the Gaussian components significantly influence the fitting degree of the GMM, we explore the correlation between the number of the Gaussian distributions and the localization error of the CPLA.
Finally, using the Gaussian distributions, burst suppression segmentation is conducted by exploiting MLE, which is probabilistically optimal.
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.
Gamma prior distribution parameters were assumed for the variances of the Gaussian distributions, and a plot of the history of the simulation was used to determine the number of iterations required for the process to equilibrate.
In this paper, in order to lay the goundwork for further analysis of mobile radio systems, a random selection of mobile locations is assumed, and the cdf of the link distances is found for two simple but fundamental scenarios: (1) a rectangular deployment area in which mobiles are uniformly distributed and (2) a deployment in which 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.
Based on this observation, we are able to approximate the belief of target position by a Gaussian distribution, i.
On the other hand, using great number of Gaussian distributions in the modeling, in addition to posing high cost of training the data, requires more parameters, otherwise the model parameters will not be well adjusted.