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  2. Central limit theorem - Wikipedia

    en.wikipedia.org/wiki/Central_limit_theorem

    The central limit theorem may be established for the simple random walk on a crystal lattice (an infinite-fold abelian covering graph over a finite graph), and is used for design of crystal structures.

  3. X-bar chart - Wikipedia

    en.wikipedia.org/wiki/X-bar_chart

    For the purposes of control limit calculation, the sample means are assumed to be normally distributed, an assumption justified by the Central Limit Theorem. The X-bar chart is always used in conjunction with a variation chart such as the x ¯ {\displaystyle {\bar {x}}} and R chart or x ¯ {\displaystyle {\bar {x}}} and s chart .

  4. Law of the iterated logarithm - Wikipedia

    en.wikipedia.org/wiki/Law_of_the_iterated_logarithm

    The law of iterated logarithms operates "in between" the law of large numbers and the central limit theorem.There are two versions of the law of large numbers — the weak and the strong — and they both state that the sums S n, scaled by n −1, converge to zero, respectively in probability and almost surely:

  5. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    Comparison of probability density functions, () for the sum of fair 6-sided dice to show their convergence to a normal distribution with increasing , in accordance to the central limit theorem. In the bottom-right graph, smoothed profiles of the previous graphs are rescaled, superimposed and compared with a normal distribution (black curve).

  6. Illustration of the central limit theorem - Wikipedia

    en.wikipedia.org/wiki/Illustration_of_the...

    This section illustrates the central limit theorem via an example for which the computation can be done quickly by hand on paper, unlike the more computing-intensive example of the previous section. Sum of all permutations of length 1 selected from the set of integers 1, 2, 3

  7. Characteristic function (probability theory) - Wikipedia

    en.wikipedia.org/wiki/Characteristic_function...

    The characteristic function approach is particularly useful in analysis of linear combinations of independent random variables: a classical proof of the Central Limit Theorem uses characteristic functions and Lévy's continuity theorem. Another important application is to the theory of the decomposability of random variables.

  8. Log-normal distribution - Wikipedia

    en.wikipedia.org/wiki/Log-normal_distribution

    This is justified by considering the central limit theorem in the log domain (sometimes called Gibrat's law). The log-normal distribution is the maximum entropy probability distribution for a random variate X —for which the mean and variance of ln( X ) are specified.

  9. Gaussian random field - Wikipedia

    en.wikipedia.org/wiki/Gaussian_random_field

    Where applicable, the central limit theorem dictates that at any point, the sum of these individual plane-wave contributions will exhibit a Gaussian distribution. This type of GRF is completely described by its power spectral density , and hence, through the Wiener–Khinchin theorem , by its two-point autocorrelation function , which is ...