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This is a "one pass" algorithm for calculating variance of n samples without the need to store prior data during the calculation. Applying this method to a time series will result in successive values of standard deviation corresponding to n data points as n grows larger with each new sample, rather than a constant-width sliding window calculation.
The standard deviation of the distribution is (sigma). A random variable with a Gaussian distribution is said to be normally distributed , and is called a normal deviate . Normal distributions are important in statistics and are often used in the natural and social sciences to represent real-valued random variables whose distributions are not ...
In the empirical sciences, the so-called three-sigma rule of thumb (or 3 σ rule) expresses a conventional heuristic that nearly all values are taken to lie within three standard deviations of the mean, and thus it is empirically useful to treat 99.7% probability as near certainty.
Full width at half maximum. In a distribution, full width at half maximum (FWHM) is the difference between the two values of the independent variable at which the dependent variable is equal to half of its maximum value.
A common example of a sigmoid function is the logistic function, which is defined by the formula: [1]
This approximate formula is for moderate to large sample sizes; the reference gives the exact formulas for any sample size, and can be applied to heavily autocorrelated time series like Wall Street stock quotes. Moreover, this formula works for positive and negative ρ alike. [12] See also unbiased estimation of standard deviation for more ...
Sigma (/ ˈ s ɪ ɡ m ə / SIG-mə; ... In machine learning, σ is used in the formula that derives the Sigmoid function. In radar jamming or electronic warfare ...
the Leibniz formula for its determinant is, using sigma notation for the sum, ... the matrix determinant lemma allows rapid calculation of the determinant of A ...