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I asked this question on, however I received no answer for a while so I'm posting it here:

Lets assume two groups of random variables X and Y (the dimensionality of them is not important). I know probability distribution of X, but not of Y. I also know that Y is a function of X and they are related through system of nonlinear equations e.g. $$ \left\\{\begin{matrix} Y_{1}^2+X_{2}=10\\\ Y_{2}^2+Y_{1}=X_{1} \end{matrix}\right. $$ Suppose for this particular example that we cannot find analytic solutions of $Y$ in terms of variables $X$. Is there any theory that would enable to extract some probabilistic information (e.g. expectation, variance) about $Y$ without first finding analytic solutions? I.e. if $X_{1}$ and $X_{2}$ are independent random variables with gamma distributions, what tools could be used to infere for example at least expectation and variance on $Y_{1}$ and $Y_{2}$ without solving system explicitly? (The example is just for illustratory purposes, the real system of equations are different.)

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suppose this would be possible, find the expectation of $Y$ without knowing how it depends on $X$, and imagine $X$ has a delta-function distribution, then magically you would have found the function $Y(X)$; how could that work? – Carlo Beenakker May 2 '13 at 11:12
Well, I do know something about $Y$ - this information is contained in the set of equations – Tomas May 3 '13 at 18:36

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