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Hello, I wish for someone to help me explain (with a code example) how i can generate a distribution around a value (say 0.007695) using Monte Carlo simulation. I understand that I might first need to generate set of random variables using 0.007695 as control.

I will be grateful to get a response. Am just a basic user of matlab

Jeff Miller
on 27 Oct 2018

The Gaussian distribution has two parameters, mean and standard deviation. Once you decide on values for those, it is easy to generate a sample of random numbers. For example:

mu = 0.0076; % the mean you asked for

sigma = 0.0003; % adjust to make the distribution as wide as you want.

samplesize = 1000;

randvals = randn(samplesize,1)*sigma + mu;

histogram(randvals);

Jeff Miller
on 31 Oct 2018

Look at the documentation for histcounts. I think you can get those numbers from something like

edges = 0:0.01:1;

[~,~,bins] = histcounts(rands,edges);

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possibility
on 26 Oct 2018

Generally, the way it works is you already possess the data samples (say millions of them) and find the histogram of the data (frequency of the values that appears in the data). After that, you pick the optimum distribution model (Gaussian, Laplacian, chi-square, etc.) that fits best to your samples.

But, if you're trying to ask "how to generate samples from a distribution with a given statistical parameter (for ex: mean)", that would have meaningful answers.

One example,

randn

command generates samples from a gaussian distribution. Or

rand

generates samples from a uniform distribution. Just type

help rand

for details.

Hope that helps.

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