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copied!<p><em>EDIT:</em> Since 12 August 2011 we have <a href="http://en.wikipedia.org/wiki/C++11" rel="nofollow noreferrer">C++11</a> which directly offers <a href="http://en.cppreference.com/w/cpp/numeric/random/normal_distribution" rel="nofollow noreferrer"><code>std::normal_distribution</code></a>, which is the way I would go today.</p> <p><em>Here's the original Answer:</em></p> <p>Here are some solutions ordered by ascending complexity.</p> <ol> <li><p><strong>Add 12 uniform numbers</strong> from 0 to 1 and subtract 6. This will match mean and standard deviation of a normal variable. An obvious drawback is that the range is limited to +/-6 - unlike a true normal distribution.</p></li> <li><p><strong>Box-Muller transform</strong> - was listed above, and is relatively simple to implement. If you need very precise samples however, be aware that the Box-Muller transform combined with some uniform generators suffers from an anomaly called Neave Effect. <p> H. R. Neave, “On using the Box-Muller transformation with multiplicative congruential pseudorandom number generators,” Applied Statistics, 22, 92-97, 1973</p></li> <li><p>For <strong>best precision</strong> I suggest <strong>drawing uniforms and applying the inverse cumulative normal distribution</strong> to arrive at normally distributed variates. You can find a very good algorithm for the inverse cumulative normal distribution at</p></li> </ol> <p><a href="https://web.archive.org/web/20151030215612/http://home.online.no/~pjacklam/notes/invnorm/" rel="nofollow noreferrer">https://web.archive.org/web/20151030215612/http://home.online.no/~pjacklam/notes/invnorm/</a></p> <p>Hope that helps</p> <p>Peter</p>

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