Sep
19

## Peter J Acklam inverse normal cumulative distribution

Random number generation is essential for Monte Carlo simulation, among random numbers, normal distributed numbers are undoubtedly the most widely used ones, here comes the problem, for a given uniform random numbers series, how do you compute the inverse normal cumulative distribution function?

I once introduced Moro inverse normal function for this purpose, here is another power function named

Good, here is the page for Peter J Acklam inverse normal cumulative distribution codes in several languages, http://home.online.no/~pjacklam/notes/invnorm/index.html#The_algorithm, enjoy.

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I once introduced Moro inverse normal function for this purpose, here is another power function named

**Peter J Acklam inverse normal cumulative distribution**, for my study and work i have tried both but couldnot decide which one is better, here i quote the sentence from the book "Monte carlo methos in finance" by Peter Jackel: Equally, for the inverse cumulative normal function z = N'(p), there are several numerical implementations providing different degrees of accuracy and efficiency. A very fast and accurate approximation is the one given by Boris Moro in [Mor95]. The most accurate whilst still highly efficient implementation currently freely available, however, is probably the algorithm by Peter Acklam. when allowing for an additional evaluation of a machine-accurate cumulative normal distribution function, Acklam’s procedure is able to produce the inverse cumulative normal function to full machine accuracy by applying a second stage refinement using Halley’s method.Good, here is the page for Peter J Acklam inverse normal cumulative distribution codes in several languages, http://home.online.no/~pjacklam/notes/invnorm/index.html#The_algorithm, enjoy.

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