#StackBounty: #dimensionality-reduction #joint-distribution #information-theory #theory Combining subjoint distributions to create a la…

Bounty: 50

I am trying to construct large joint distributions through smaller joint distributions and I’m not sure how to approach the literature.

I am curious if there exists a function which can take n subjoint distributions and approximate a larger joint distribution.

f(P(X,Y), P(X,Z)) = P(X,Y,Z) + e

I am also curious whether overlapping variables (variables in both sub joint distributions) lower the error in comparison to disjoint sub joint distributions.

P(X,Y,Z,A) - f(P(X,Y,A), P(X,Z)) < P(X,Y,Z,A) - f(P(X,Y), P(Z,A))?

I have a feeling that this has something to do with inclusion-exclusion and mutual information.

Get this bounty!!!

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