Variational Inference: Simple Example (+ Python Demo)

Variational Bayesian Methods can be difficult to understand. In this video, we will look at the simple Exponential-Normal model for which the posterior is intractable. We will show why and then propose a surrogate and perform VI. Here are the notes: https://github.com/Ceyron/machine-lea...

Find the visualization here: https://share.streamlit.io/ceyron/mac...

Variational Inference is a powerful technique in Machine Learning that is used to find approximate posteriors for generative models. In particular, it is being used extensively for Variational Autoencoders.

-------

📝 : Check out the GitHub Repository of the channel, where I upload all the handwritten notes and source-code files (contributions are very welcome): https://github.com/Ceyron/machine-lea...

📢 : Follow me on LinkedIn or Twitter for updates on the channel and other cool Machine Learning & Simulation stuff:   / felix-koehler   and   / felix_m_koehler  

💸 : If you want to support my work on the channel, you can become a Patreon here:   / mlsim  

-------

Timestamps:
00:00 Introduction
00:38 Agenda
01:30 Joint distribution
04:49 Trying to find the true posterior (and fail)
14:45 Visualization (Joint, Posterior & Surrogate)
19:05 Recap: Variational Inference & ELBO
21:56 Introducing a parametric surrogate posterior
24:45 Remark: Approximating the ELBO by sampling
27:24 Performing Variational Inference (Optimizing ELBO)
38:38 Python example with TensorFlow Probability
47:09 Outro