In the Fall of my first quarter (Fall 2015) as a grad student at UW, I was working on a project that involved stochastic variational methods on time series models. But I hadn’t learned variational inference, so I spent a few weeks reading up on it and deriving some variational methods for some basic graphical models. This really helped me learn the material well, and I decided to write an in-depth tutorial to help others cross the bridge I crossed. Most tutorials/introductions I’ve seen about this topic skip a lot of details. While it’s great to spent time filling in those details on your own (which is what I did), I figured there should be something out there that does have all the microscopic details, so I wrote this tutorial.

Here is the PDF: In-Depth Variational Inference Tutorial. There is a corresponding github repository to accompany the tutorial which can be found here.

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