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Computational Efficiency of Polar and Box Muller Method: Using Monte Carlo Application

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  • July 2012
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Computational Efficiency of Box-Muller and Polar Method
Using Monte-Carlo Application

by : Joy V. Lorin-Picar
Mathematics Department
Davao del Norte State College, New Visayas, Panabo City

The efficiency of Mean Square Error (MSE) of the random normal variables generated from both the Marsaglia Polar Method and Box-Muller Method was examined for small and large n with Monte-Carlo application using MATHLAB. The empirical results showed that MSE of the random normal variables using the Marsaglia Polar Method approaches zero as n becomes larger.

Moreover, when run in MATHLAB, the Box-Muller method encountered some problems like: a) it runs slow in generating its MSE because of many calls to the math library; b) it has numerical stability problems when x1 is very close to zero; as a consequence of b, as n becomes large, there are serious problems if you are doing stochastic modeling and generating millions of numbers. Apparently, the Polar Method computes the MSE faster even when n is large, since it does the equivalent of the sine and cosine geometrically without a call to the trigonometric function library.

Keywords: Mean Square Error (MSE), Marsaglia Polar Method, Box-Muller Method, Monte-Carlo application

The topic of generating Gaussian pseudo-random numbers given a source of uniform pseudo-random numbers comes up more frequently. There are many ways of solving this problem but this paper focuses through the Box-Muller and Marsaglia Polar Methods.

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