julia

Lots of distributions are easy to evaluate (the density), but hard to sample. So when we need to sample such a distribution, we need to use some tricks. We'll see connections between two of these: importance sampling and variational inference, and see a way to use them together for fast inference. Importance sampling Importance sampling aims to make it easy to compute expected values. Say we have a distribution \(p\), and we'd like to compute the average of some function \(f\) of the distribution (or equivalently, the expected value of a "push-forward along \(f\)"). [Read More]

If you've followed my work recently, you've probably heard of my probabilistic programming system Soss.jl. I recently had the pleasure of presenting these ideas at PyData Miami: [N.B. Above is supposed to be an embedded copy of my slides from PyData Miami. I can see it from Chrome, but not Firefox. Very weird. ] In April I'll begin another "passion quarter" (essentially a sabbatical) and hope to really push this work forward. [Read More]

Since around 2010, I've been involved with using and developing probabilistic programming languages. So when I learn about new language, one of my first questions is whether it's a good fit for this kind of development. In this post, I'll talk a bit about working in this area with Julia, to motivate my Soss project. Domain-Specific Languages At a high level, a probabilistic programming languages is a kind of domain-specific language, or DSL. [Read More]