Say we have two random variables X,Y with mutual information I(X,Y). Let’s say we want to represent them with a bayes net of the form X< -M->Y, such that the entropy of M equals the mutual information, i.e. H(M)=I(X,Y). Intuitively, we would like our hidden state to be as simple as possible (entropy wise). The data processing inequality means that H(M)>=I(X,Y), so the mutual information is a lower bound on how simple the M could be. Furthermore, if such a construction existed it would have a nice coding interpretation — one could jointly code X and Y by first coding the mutual information, then coding X with this mutual info (without Y) and coding Y with this mutual info (without X).
It turns out that such a construction does not exist in general (Thx Alina Beygelzimer for a counterexample! see below for the sketch).
What are the implications of this? Well, it’s hard for me to say, but it does suggest to me that the ‘generative’ model philosophy might be burdened with a harder modeling task. If all we care about is a information theoretic, compact hidden state, then constructing an accurate Bayes net might be harder, due to the fact that it takes more bits to specify the distribution of the hidden state. In fact, since we usually condition on the data, it seems odd that we should bother specifying a (potentially more complex) generative model. What are the alternatives? The information bottleneck seems interesting, though this has peculiarities of its own.
Here is the joint distribution P(X,Y). Sample binary X from an unbiased coin. Now choose Y to be the OR function of X and some other ‘hidden’ random bit (uniform). So the joint is:
Note P(X=1)=1/2 and P(Y=1)=3/4. Here,
I(X,Y)= 3/4 log (4/3) ~= 0.31
The rest of the proof showing that this is not achievable in a ‘compact’ Bayes net is in a comment.