authored by mimee@unify.id (prev. @Baidu Silicon Valley AI Lab), Oct 23, 2017

## Core Ideas ✨

**Inference in a nutshell:**

Computing posterior distribution

$$p(\vec{h}|\vec{v})$$

where v is observed data, and h is latent.

- Exact inference is hard / often intractable

Here, we (mostly) see inference as optimization by augmenting **p** with a distribution **q** on latent **h** —

ELBO

## General Resources ✨

**Book Chapter:**

Presentation Script/Notes

## Next Up - Methods 🚶

E-M

MAP / Sparse Encoding

Variational Inference and Learning

Sampling based methods

Wake-Sleep

## Stretch goal reading 🏃

Marginal Likelihood (what we are trying to bound in the very beginning)

Marginal likelihood - Wikipedia

E-M intro (behind paywall :( )

What is the expectation maximization algorithm?

Auto-encoding variational bayes

https://www.youtube.com/watch?v=rjZL7aguLAs

(unevaluated, but interesting) Adversarially learned inference

[1606.00704] Adversarially Learned Inference

New paper argung that SGD implicitly performs variational inference