normally random number generator with limited zone

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milad babaei 2011-2-25

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tnx in advance, i have 3 random variables.that every of that 3 related to a another(*) one(of course)its random too.i use normal distribution generator for monte carlo simulation in matlab to produce RAndom number.but in some cases with mention to unlimited zone of generating random number my final data(*)will be negative.that is not accurate and unexplainable for my job(bearing capacity-geotechnical eng.)i choose normally dist. because its the best match for my data and bell type format of its distribution also help me and i have to use it..also i need to generate positive random number with this normal dist.i think i should find a way to limit this generator to produce random number between -1 & 1 or even find a way to use this distribution with desirable mean and standard deviation..

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Walter Roberson 2011-5-29

This question duplicates http://www.mathworks.com/matlabcentral/answers/2028-normal-random-number-generator-with-specified-zone

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Bruno Luong 2011-2-25

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This function will generate a normal distribution conditional by bounds:

http://www.mathworks.com/matlabcentral/fileexchange/23832-truncated-gaussian

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milad babaei 2011-3-2

I mean if i have three variable c,g,fi that each of them has e relation like this q:F(c,g,fi)...so i need to use a for loop to provide the distribution of q

milad babaei 2011-3-3

if i want to use this new code(with peakpos) on that original m_file,where should i change??in which line???plz help mee

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Manu R 2011-2-25

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You should not choose a probability distribution and then restrict its range. It leads to all sorts of problems with your measures of randomness (higher order moments) being flawed later.

I suggest you pick a non-negative distribution that resembles the Gaussian on the positive scale. I'd point you to the Exponential distribution and the Nakagami distribution to start your research on, solely based on your description so far.

Then, you can use the Normal distribution to generate numbers for these other distributions through an inverse transform.