@junpenglao Really loved the talk and all the details of how to think of missing values from a Bayesian perspective. Interestingly, I am recording a talk for PyData Global in a couple of weeks, which also tries to look at missing value imputation as Bayesian inference. I focus on the issues that simplistic imputation was causing me in my work, and talk about the “iterative imputer” in sklearn that I used to impute (and how the iterative imputer is doing approximate inference in a Bayesian model).
Don’t have a link to the talk yet, but check out the slides if you are interested: https://narendramukherjee.github.io/blog/when-features-go-missing-bayes-comes-to-the-rescue/
Also, I have added a link to your tutorial in my slides for people to get a deeper understanding of the topic 