I would greatly appreciate any of you who could help me with this challenge. I am going to state the problem in sequential order, so as to make it clear:
I have $n$ normally distributed random variables.
I have statistical data for each random variable (thus, their mean and variance).
I have historical data for each of the random variables, and from these data I can calculate the correlation between the random variables.
Now, assuming I know the value of one of the variables, how do I select the values of the other variables, such that all the $n$ values in the resulting vector are correlated accordingly. If that is possible, how do I extend this to a situation where I have two or more known values? For example, I know two or more values out of the $n$ variables (they satisfy the correlation constraint), and want to pick the others so that they also satisfy the correlation and their respective distribution functions.
I am not well-versed in statistics, so I would appreciate explanations that are as simple to understand as possible.