# Variational Inference (part 1)

I will dedicate the next few posts to variational inference methods as a way to organize my own understanding – this first one will be pretty basic.

The goal of variational inference is to approximate an intractable probability distribution, , with a tractable one, , in a way that makes them as ‘close’ as possible. Let’s unpack that statement a bit.

- Intractable : a motivating example is the posterior distribution of a Bayesian model, i.e. given some observations and some model parameterized by , we often want to evaluate the distribution over parameters
For a lot of interesting models this distribution is intractable to deal with because of the integral in the denominator. We can evaluate the posterior up to a constant, but we can’t compute the normalization constant. Applying variational inference to posterior distributions is sometimes called variational Bayes.

- A tractable posterior distribution is one for which we can evaluate the integral (and therefore take expectations with it). One way to achieve this is by making each independent:
for some marginal distributions parameterized by . This is called the ‘mean field’ approximation.

- We can make two distributions ‘close’ in the Kullback-Leibler sense:
Notice that we chose and not because of the intractability – we’re assuming you cannot evaluate .

In order to minimize the KL divergence , note the following decomposition

where falls out of the expectation with respect to . This is convenient because (the log evidence) is going to be constant with respect to the variational distribution and the model parameters .

And because KL divergence is strictly nonnegative, the second term is a lower bound for , also known as the evidence lower bound (ELBO). In order to minimize the first term, , it suffices to maximize the second.

One way to optimize over the choice of is to consider the ELBO with respect some , separating it from the expectation with respect to all other variables, (note that is the integral over all with ):

where

This motivates a particular updating scheme: iterate over the marginals , maximizing the ELBO at each step with , and repeat.

These statements remain pretty general. We haven’t specified the functional form of , but it will fall out of the for specific models (a good, simple example is the normal-gamma model from Murphy’s MLAPP or Bishop’s PRML). This form will then define the variational parameters , and the iterative algorithm will provide a way to compute them.

Some fun properties of the mean field approximation:

- The optimization function is not guaranteed to be convex (wainwright and jordan)
- The optimization procedure is pretty much out of the box for exponential family models (maybe a future blog post will be dedicated to exploring this)
- This variational approximation underestimates uncertainty (a consequence that pops out as a result of the KL divergence ordering, as opposed to ).

This begs the question, can you do better with richer distributions? Yes! In future posts, I will take a look at some more complicated variational distributions and their performance.

references:

- Kevin Murphy. Machine learning: A probabilistic perspective
- Wainwright and Jordan. Graphical Models, Exponential Families, and Variational Inference