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January 8th, 2012, 10:52 PM  #1 
Newbie Joined: Jan 2012 Posts: 2 Thanks: 0  random number generation with normal distribution
Dear all Hi, I seek the formulation of generating a random number whithin for example 8<x<12 but having the normal distribution. In Excel there is a built in function to do that which is "norm_rnd". But I need the formulation of creating such number Sincerely Nasser 
January 9th, 2012, 12:45 AM  #2 
Newbie Joined: Jun 2011 Posts: 5 Thanks: 0  Re: random number generation with normal distribution
Hey, You can do this is several ways. Depending on what you want to achieve, here are some possibilities: 1. Based on the central limit theorem, you can add up an (arbitrary) large amonut of uniformly distributed numbers, and then scale them to your desired mu & sigma. The more numbers you add, the more it will approach the normal distribution. 2. This is a more general approach (it works for about any distribution), is to take the CDF (cumulative distribution function) of the distribution (in this case, an "erf" function). Next, you uniformly generate a random number between 0 and 1, and then take the INVERSE CDF function value of that random generated number. i.e.: You generated y (between 0 & 1), given that the CDF of the normal distribution equals to " 1 + erf( (xmu)/(sigma*sqrt(2)) ) ", then by solving it to x: y = 1 + erf( (xmu)/ ( sigma*sqrt(2)) ), or erf^1(y1) * sigma*sqrt(2) + mu = x By filling in a random generated y, you will get a randomly normal distributed x out of a PDF with mean "mu" and standard deviation "sigma". To keep the value between say, 8 and 12, you can either: * choose your interval for y in such a way that it only can return values for x between 8 and 12. (more elgant) * put it in a while loop, until it returns a value between 8 and 12. (ugly but easier) 
January 9th, 2012, 03:18 PM  #3 
Global Moderator Joined: May 2007 Posts: 6,730 Thanks: 689  Re: random number generation with normal distribution
There is an analytic method to generate two random variables, which are independent and standard normal. This is based on the fact that by using polar coordinates the joint density function for x and y (normal) can be converted into a joint density for u and ?, where u is distributed exponentially and ? is uniform over the unit circle. Then let r=?u, so X=rcos? and Y=rsin?.


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