Normal Distribution

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Related to Gaussian Distributions: Gaussian random variable

Normal Distribution

The well known bell shaped curve. According to the Central Limit Theorem, the probability density function of a large number of independent, identically distributed random numbers will approach the normal distribution. In the fractal family of distributions, the normal distribution only exists when alpha equals 2, or the Hurst exponent equals 0.50. Thus, the normal distribution is a special case which in time series analysis is quite rare. See: Alpha, Central Limit Theorem, Fractal Distribution.

Bell Curve

A curve on a chart in which most data points cluster around the median and become less frequent the farther they fall to either side of the median. When plotted on a chart, a bell curve looks roughly like a bell.
References in periodicals archive ?
11 shows the impact of the number of the Gaussian distributions on localization error.
After modeling of the distributions of burst and suppression of each feature into a Gaussian distribution, we employed the method of maximum likelihood estimation (MLE) to conduct the burst suppression segmentation.
Some causal processes can simulate Gaussian distribution.
We will use the Gaussian distribution since optimising the logarithm of this pdf is straightforward.
The distribution of the differences between the traditional and continuous wind speed data somewhat resemble the Gaussian distribution.
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.
Then, the goal is to find [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] in a given class R of Gaussian distribution to minimize the KLD
Figure 1 displays HTER for different number of Gaussian distributions as a graph.
i], as estimated from healthy (or diseased) individuals during steady state, assuming gaussian distributions and variance homogeneity.
In this paper, we thus discuss and compute the variousparameters involved in characterizing the real and complex Gaussian distributions completely for one andmultiple Random Variables (RVs).
Halgreen, 1977, Infinite Divisibility of the Hyperbolic and Generalized Inverse Gaussian Distributions, Zeitschrift fur Wahrscheinlichkeitstheorie und verwandte Gebiete, 38:439-455.
Comparing obtained results with uniform and Gaussian distributions of [beta] values, it could be seen that despite negligible difference in minimum mean error, about 1 %, the difference in number of optimal solutions found is about 2 times for uniform distribution and about 1.