# Is outer product of marginal distribution the "best" mean-field approximation for a joint distribution?

I am certain this has been asked somewhere else, if that's the case, link me and close the thread.

I am studying variational inference and mean-field approximation. All the explanations I come across deal with gaussians and continuous distributions which are too much for me to handle right now. I want to just understand the simplest case of discrete distributions first, and I'm unable to find the resource online, so here's where I need help.

The set up is as follows. We are trying to approximate a joint with two factors $$P(X,Y) \approx Q_1(X)Q_2(Y)$$. One way to do so (with "modern" technology aka pytorch) is to set up a loss function $$KL(Q_1(X)Q_2(Y) ~||~ P(X,Y))$$ and optimize it under the constraint that $$Q1$$ and $$Q2$$ are valid probability distributions.

Here are my questions:

Question 1

Is the optimal solution for $$Q_1$$ and $$Q_2$$ simply the respective marginals of $$P(X,Y)$$, $$P(X)$$ and $$P(Y)$$? Is this still the case if we were to use a different loss function that is not KL (i.e. L2, weisserstein) ? I am feeling there is some quirks about KL that could make this bit more pathological.

Question 2

If we can only sample batches from the joint distribution $$P(X,Y)$$, how might we learn $$Q_1$$ and $$Q_2$$ in a sgd style? Should we optimize $$Q_1$$ and $$Q_2$$ together, or, assume the answer to question 1 is true, sample a bunch of data points from $$P(X,Y)$$ and create the sampled marginal distribution $$\hat{P}(X)$$ and $$\hat{P}(Y)$$ and optimize $$Q_1$$ and $$Q_2$$ separately?

Thanks a ton!

This is really two questions, and I'm only going to address the first one: No.

There's a nice illustration here from Eric Jang for a simpler case: approximating a Gaussian mixture by a single Gaussian, and, yes, it is related to the special properties of the KL divergence. In particular, $$KL(Q_xQ_y||P)$$ is infinite if there is any point where $$Q_xQ_y$$ places mass but $$P$$ places no mass.

Using $$KL(Q_xQ_y||P)$$ as your objective function means the top priority for optimisation is having the density $$Q_xQ_y$$ be zero whenever the density $$P$$ is zero, and more generally having $$Q_xQ_y$$ small whenever $$P$$ is small. Now, consider real $$X$$, $$Y$$ where $$P$$ is uniform on a ring centered at the origin, a sort of thickened unit circle. The marginal distributions are U-shaped, but with non-negligible probability near zero. The product of the marginals has density everywhere on a square, higher at the edges and lower in the middle, but non-zero everywhere. So, near the origin, $$Q_xQ_y$$ is non-zero and $$P$$ is zero and $$KL(Q_xQ_y||P)$$ is infinite.

In this example even the optimal product distribution will be a terrible approximation, but it is possible to construct products that have finite $$KL(Q_xQ_y||P)$$, which is better than infinite.

You might object that you are interested in distributions that have non-zero density everywhere, but that just complicates the analysis; there will still be examples where $$KL(Q_xQ_y||P)$$ for the product of marginals is finite but very large.

• this is a fantastic answer, thank you very much. I'm actually quite okay with the "square donut" of P(X)P(Y) being like that, it seems it has support whenever P(X,Y) has support, and I'm okay with that. Maybe I'll use a different metric beyond KL. Sep 4, 2021 at 6:42