Applying the inverse transform method to the normal distribution entails evaluation of the inverse normal. This is the Beasley-Springer-Moro algorithm for approximating the inverse normal.
Input: u, a sacalar or matrix with elements between 0 and 1
Output: x, an approximation for the inverse normal at u
Pau Glasserman, Monte Carlo methods in financial engineering, vol. 53 of applications of Mathematics (New York),
Springer-Verlag, new York, 2004, p.67-68
Wolfgang Putschögl (2022). Approximating the Inverse Normal (https://www.mathworks.com/matlabcentral/fileexchange/28988-approximating-the-inverse-normal), MATLAB Central File Exchange. Retrieved .
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