Hello everybody, I am quite a beginner and I am sorry if my question seems trivial, but I hope somebody will help me.
Let's assume I have a quantity Q which is function of n inputs Xi:
Q = f(X1, X2, … Xn)
Now, let's assume that some of these inputs are distributed according to a Gaussian. Thus, for example, X1, X2 and X3 are randomly distributed with a well define mean value and standard deviation, while X4 … Xn are instead assumed to be constant.
I know how to generate randomly the populations X1, X2 and X3 on Matlab, with a command that should implement implicitly a Monte Carlo approach:
pop_X1 = X1_nom + randn(N,1) * X1_dev;pop_X2 = X2_nom + randn(N,1) * X2_dev;pop_X3 = X3_nom + randn(N,1) * X3_dev;
However, how do I generate Q taking into account all these input populations variations? Can I simply apply the function f aligning the vectors of X1, X2 and X3 previously generated?
Thanks!!
Paolo
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