Multivariate Gaussian Distribution

Calculates samples from a multivariate Gaussian distribution.

Description

Creates a number of samples from a specified number of dimensions and centers them around a given mean, and within a given covariance range. You might not find it very useful, but hey, I need something to do this so why not.

To use:
You need to generate 1000 samples from a 3 dimensional Gaussian distribution with a mean m = [4,5,6], and with a covariance sigma = [9 0 0;0 9 0;0 0 9].

Command line:
x=mgd(1000,3,m,sigma) or x=mgd(1000,3,m',sigma)
it doesn't matter if the mean is given as a row or column vector

x is a (1000x3) matrix of the where
where each row is the coordinates of that point in 3 space.

• Acknowledgements: This submission has inspired the following:; Gaussian Random Samples Generation
• MATLAB release: MATLAB 6.1 (R12.1)
• Other requirements: As far as I know it will work as long as you have randn function. Send me an email if it doesn't.

[Bob Sturm]

Works good, except when I want only a few realizations. Freezes when I ask for 1x1 result.

[lei fang]

very good, very convinient to use. helpful to finish course project

[mohamad ayat]

very good but I couldn't understand it's algorithm

[yc wang]

in 1-D normal distribution, when sigma=0, it doesn't work.

[Gee Didilly]

pa pa ooo mow mow

[Seblegit SIrgaga]

Please try to write the program with out a for loop. Matlab is a good software for matrix manupulation. FOr loops make programs slower. Just use,
x=x*sqrt(variance)+ones(N,1)*mean(x)

[Daniel Mark]

Thank you so much for your code!

Daniel

[Timma Gupta]

thank you..!

[hicham laanaya]

thanks a lot!

[ahmed ased]

 

[Sherman Kuo]

Excellent Work !!!

[Matteo Zandi]

Works even with d=2, thanks

[Dan McClanahan]

Code works well, except....
Generating 2D gaussian works great when I create a vector of samples. However, let's say I want to generate N samples but rather than generating all N at once I make N successive calls to the mgd function. Here, the function breaks down.

[Tim Felty]

Dan McClanahan: The problem with mean stems from the fact that the code subtracts off the current sample mean assuming that its going to be two dimensional as well.

The covariance issue seems to be cause by the single sample getting multiplied by the covariance matrix. As to why this is causing problems I'm not to sure. But since that is the case it might be better to generate your samples directly from the distribution.

[Tim Felty]

Dan McClanahan: Actually upon further testing in the situation you describe the error seems to be cause only by the failure to account for the calculated mean of one sample in the code being a scalar.

[hendri hendri]

 

[Tom Brady]

A waste of time trying to use this file. Better write it yourself. It generates totally wrong distributions. DO NOT WASTE YOUR TIME.

[maria papadopoyloy]

 

[M Taj]

DO NOT WASTE YOUR TIME. The code looks wrong

[Ahmed Fasih]

Use "x=bsxfun(@plus, x, rmean)" instead of repmat.

[Xiu]

is mgd a built-in matlab function?

[Krishna Kumar Kottakki]

excellent.....thanks allot.