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Approximating the Inverse Normal

version 1.2.0.0 (1.68 KB) by Wolfgang Putschögl
Beasley-Springer-Moro algorithm for approximating the inverse normal.

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Updated 08 Aug 2011

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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

Reference:
Pau Glasserman, Monte Carlo methods in financial engineering, vol. 53 of applications of Mathematics (New York),
Springer-Verlag, new York, 2004, p.67-68

Cite As

Wolfgang Putschögl (2020). Approximating the Inverse Normal (https://www.mathworks.com/matlabcentral/fileexchange/28988-approximating-the-inverse-normal), MATLAB Central File Exchange. Retrieved .

Comments and Ratings (2)

Aris

Amazing implementation! Thank you very much!

Ben Petschel

any comments on the speed or accuracy compared to norminv? e.g. at u=0.917 the values differ by about 1-e9.

Updates

1.2.0.0

Thanks to Ben Petschel who provided optimized code that brings it almost on par with the builtin (compiled) norminv/erfcinv!

1.1.0.0

Thanks to Ben Petschel who provided optimized code that brings it almost on par with the builtin (compiled) norminv/erfcinv!

MATLAB Release Compatibility
Created with R2010a
Compatible with any release
Platform Compatibility
Windows macOS Linux