For me first understanding SSM (the model) and then Bayesian view on filtering and seeing Kalman filter an “algo” for LGSSM (model) was useful. I explain it here - youtu.be/-DiZGpAh7T4?si=raLU…

An in-depth explanation of challenges and one potential remedy for gradient estimation in stochastic computational graphs youtu.be/nKM9875PVtU

Variational Autoencoder, Latent Variable Model & Amortized Inference... youtu.be/h9kWaQQloPk

What is Probabilistic Programming? + A quick & opinionated review of @mcmc_stan @pymc_devs @PyroAi @TuringLang @TensorFlow Probability (TFP) youtu.be/-C8PGgnF1yg using an example from @rlmcelreath's Statistical Rethinking book

The Metropolis Algorithm. Watch the full tutorial (The Metropolis-Hastings Algorithm) on youtube (youtube.com/watch?v=oX2wIGSn…)

Published a tutorial on Importance Sampling youtu.be/ivBtpzHcvpg

A visual introduction to Rejection Sampling! Full tutorial on YouTube youtu.be/si76S7QqxTU

Published a video explaining "MADE: Masked Autoencoder for Distribution Estimation" youtu.be/7q4ueFiJjAY MADE is an essential component in many modern Normalizing Flows algorithms

Published a comprehensive tutorial on Normalizing Flows - Motivations, The Big Idea, & Essential Foundations youtu.be/IuXU2dBOJyw