MATLAB: How to calculate the probabilistic constraints in Matlab

normal distributionrandom variable

I have random variables x which follow Normal Distribution with mean and variance and I want to calculate the probablitiy of P(x<= a1+a2+a3)=0.9 where a1, a2, a3 are unknown variables. How do I calculate using Matlab?

Best Answer

a1+a2+a3 = norminv(0.9,mu,sigma)

https://de.mathworks.com/help/stats/norminv.html

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MATLAB: Evaluate a distribution programmatically

The fitdist (link) function will fit a distribution to data, producing a probability distribution object:

pd = fitdist(x,distname)

You can then calculate the probabilities with it using the Plot Standard Normal Distribution cdf (link) or Plot the pdf of a Standard Normal Distribution (link) functions:

data = 5 + 2*randn(1, 100);                         % Create Data
pd = fitdist(data(:), 'Normal');
x = linspace(-2, 12);
Npdf = pdf(pd, x);
Ncdf = cdf(pd, x);
figure
plot(x, Npdf,   x, Ncdf)
grid

MATLAB: What is the difference between mvnrnd and normrnd

mvnrnd() is the function used to generate pseudo random numbers that follow multi variate normal distribution. mvnrnd() is more for creating correlated random variables normrnd() is the function used to generate pseudo random numbers that follow normal distribution. normrnd() is generally faster than mvnrnd(). For more information on mvnrnd() and normrnd() follow the documentation http://www.mathworks.com/help/stats/mvnrnd.html

http://www.mathworks.com/help/stats/normrnd.html

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Related Solutions

MATLAB: Evaluate a distribution programmatically

MATLAB: What is the difference between mvnrnd and normrnd

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