gaussian distribution


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nor·mal dis·tri·bu·tion

a specific bell-shaped frequency distribution commonly assumed by statisticians to represent the infinite population of measurements from which a sample has been drawn; characterized by two parameters, the mean (x) and the standard deviation (σ), in the equation:

normal distribution

A bell-shaped frequency distribution of data, the plotted curve of which is symmetrical about the mean, indicating no significant deviation of the data set from the mean.

Properties of a normal distribution
Continuous and symmetrical, with both tails extending to infinity; arithmetic mean, mode, and median are identical. The curve’s shape is completely determined by the mean and standard deviation.

gaus·si·an dis·tri·bu·tion

(gow'sē-ăn dis'tri-byū'shŭn)
The statistical distribution of members of a population around the population mean. In a gaussian distribution, 68.2% of values fall within ± 1 standard deviation (SD); 95.4% fall within ± 2 SD of the mean; and 99.7% fall within ± 3 SD of the mean.
Synonym(s): bell-shaped curve, normal distribution.

Gaussian distribution

Normal distribution. The distribution of characteristics found in large populations subject to many causes of variability. The graph of the Gaussian distribution of any characteristic (such as body height) is a symmetrical bell shape, centred on the mean. (Johann Karl Friedrich Gauss, 1777–1855, German mathematician).

Gauss,

Johann K.F., German physicist, 1777-1855.
gauss - a unit of magnetic field intensity.
gaussian curve - a specific bell-shaped frequency distribution. Synonym(s): gaussian distribution; normal distribution
gaussian distribution - Synonym(s): gaussian curve

distribution

the arrangement of numerical data. The arrangement may be in accordance with magnitude, a frequency distribution, or in relation to geographical location, a spatial distribution.

age distribution
see age distribution.
bimodal distribution
the distribution has two regions of high frequency of observations separated by a zone of low frequency.
binomial distribution
a probability distribution associated with two mutually exclusive outcomes.
cluster distribution
a nonrandom distribution with observations aggregating about geographic or temporal variables. May be deceptive and merely reflect the distribution of an uneven population.
frequency distribution
a table or graph of the frequency of occurrence of each value of a variable.
Gaussian distribution
see normal distribution (below).
hypergeometric distribution
may apply to sampling without replacement of a finite population.
lognormal distribution
a distribution which is normal when the log values of the variable are considered.
normal distribution
a graph of the distribution appears as a bell-shaped curve which is symmetrical on the two sides of the vertical axis through the peak of the curve. Called also gaussian distribution.
parent distribution
the distribution (population) that was originally sampled.
Poisson distribution
regular distribution
distributed at regular intervals of time or space; all values within its given interval are equally likely.
sex distribution
an increase in frequency in one sex, which includes neutered males and neutered females. Called also sex-linked or sex-associated.
skewed distribution
a distribution in which the curve illustrating it is not symmetrical but has a long tail on one or other side of the graph.
spatial distribution
variations in distribution related to position in space, e.g. close to the door of a barn.
t-distribution
see t-test.
temporal distribution
variation in distribution related to time, e.g. occurrence of disease incidents after visits by veterinarians, inseminators, feed salesmen.
References in periodicals archive ?
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.
When the posterior probability of each Gaussian distribution is obtained, update the parameters [[theta].
In the third model, denoted by heterogeneous Gaussian model, the associated marks are independent and follow a Gaussian distribution with different means and different variances:
This distribution is an even function with a longer and larger tails than the Gaussian distribution with the same variance.
The Q-Q plot clearly indicates that the distribution tails for both corn and wheat are heavier than the tails of the Gaussian distribution.
As can be seen in Figure 6 the error is dependent to the tail part of the Gaussian distribution.
related to the complex Gaussian distribution with zero mean and unit
The Gaussian distribution is given in terms of the mean p and the standard deviation o as:
However, after logarithmic transformation a Gaussian distribution was obtained, and therefore we applied the Bhattacharya (11) method after logarithmic transformation.
If take a Gaussian distribution as an initial field, the beam preserves its Gaussian shape but its RMS beam width "breathes" sinusoidally as it travels in a straight path.
Therefore, we propose using a mixture of Gaussian distribution functions to represent the joint probability distribution F{Y,X), where each mode (or mixture component) will represent a locally linear regime of building performance.
Therefore, we propose using a mixture of Gaussian distribution functions to represent the joint probability distribution F(Y,X), where each mode (or mixture component) will represent a locally linear regime of building performance.

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