Approximating the Inverse Normal
Beasley-Springer-Moro algorithm for approximating the inverse normal.
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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 (2026). Approximating the Inverse Normal (https://www.mathworks.com/matlabcentral/fileexchange/28988-approximating-the-inverse-normal), MATLAB Central File Exchange. Retrieved .
General Information
- Version 1.2.0.0 (1.68 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 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! |
||
| 1.0.0.0 |
