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  2. Box–Muller transform - Wikipedia

    en.wikipedia.org/wiki/Box–Muller_transform

    The implementation below in standard C++ generates values from any normal distribution with mean and variance . If Z {\displaystyle Z} is a standard normal deviate, then X = Z σ + μ {\displaystyle X=Z\sigma +\mu } will have a normal distribution with mean μ {\displaystyle \mu } and standard deviation σ {\displaystyle \sigma } .

  3. Truncated normal distribution - Wikipedia

    en.wikipedia.org/wiki/Truncated_normal_distribution

    Implementations can be found in C, C++, Matlab and Python. Sampling from the multivariate truncated normal distribution is considerably more difficult. [11] Exact or perfect simulation is only feasible in the case of truncation of the normal distribution to a polytope region.

  4. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    The simplest case of a normal distribution is known as the standard normal distribution or unit normal distribution. This is a special case when μ = 0 {\textstyle \mu =0} and σ 2 = 1 {\textstyle \sigma ^{2}=1} , and it is described by this probability density function (or density): φ ( z ) = e − z 2 2 2 π . {\displaystyle \varphi (z ...

  5. Marsaglia polar method - Wikipedia

    en.wikipedia.org/wiki/Marsaglia_polar_method

    The Marsaglia polar method [1] is a pseudo-random number sampling method for generating a pair of independent standard normal random variables. [2]Standard normal random variables are frequently used in computer science, computational statistics, and in particular, in applications of the Monte Carlo method.

  6. Multivariate normal distribution - Wikipedia

    en.wikipedia.org/wiki/Multivariate_normal...

    If Y = c + BX is an affine transformation of (,), where c is an vector of constants and B is a constant matrix, then Y has a multivariate normal distribution with expected value c + Bμ and variance BΣB T i.e., (+,).

  7. Owen's T function - Wikipedia

    en.wikipedia.org/wiki/Owen's_T_function

    The function T(h, a) gives the probability of the event (X > h and 0 < Y < aX) where X and Y are independent standard normal random variables. This function can be used to calculate bivariate normal distribution probabilities [2] [3] and, from there, in the calculation of multivariate normal distribution probabilities. [4]

  8. Sturges's rule - Wikipedia

    en.wikipedia.org/wiki/Sturges's_rule

    Sturges's rule [1] is a method to choose the number of bins for a histogram.Given observations, Sturges's rule suggests using ^ = + ⁡ bins in the histogram. This rule is widely employed in data analysis software including Python [2] and R, where it is the default bin selection method.

  9. Complex normal distribution - Wikipedia

    en.wikipedia.org/wiki/Complex_normal_distribution

    The standard complex normal is the univariate distribution with =, =, and =. An important subclass of complex normal family is called the circularly-symmetric (central) complex normal and corresponds to the case of zero relation matrix and zero mean: μ = 0 {\displaystyle \mu =0} and C = 0 {\displaystyle C=0} . [ 2 ]