Suppose you know the model should be of the form y =f(x1)g(x2,x3), where f and g are the functions I'm trying to find. Essentially, x2, x3 collectively predict some hidden variable z, and the response variable y is z dampened by some function of x1. How would you encode this in a regression? Bayesian networks seem related, but don't seem as applicable to regression.
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What are f and g? – Tim Feb 06 '18 at 11:07