Generating a distribution around a parameter using monte carlo simulation.
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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
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Accepted Answer
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);
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More Answers (1)
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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