So I went through the second chapter which covers some basics around regression, and it does so from a very frequentists point of view.
Are people here familiar with frequentist stats? If not, what’s your game plan?
Try to follow the textbook but applying Bayesian methods instead? Learn the bare minimum required by the textbook material?
Curious to hear what others think!
Hey Marcin,
I’m trying to figure this one out as well but let me split it into three parts.
There are the ideas because causal inference that will have nothing to do with which estimation technique is used, whether that be Bayesian or Frequentist. Things like Randomized Control Trials, DAGs etc.
For me personally it’ll be great to learn the frequentist methods. Frequentist methods are not inherently wrong, just a different philosophy and toolkit to make estimations
Now for the last bit, how Bayesian will this book club be It’ll be as Bayesian as people make it. In this upcoming chapter I intend to try a couple of things with Bayesian methods. I’ll share a notebook or video once I do so!
The good news is we’ll learn something different with each strategy!
I got the printed book as I love me some paper, but man oh man the errata on page 22 about set unions had me questioning my existence for a while. Put me right off. So just an FYI for those using the printed copy.
He does mention it in Errata
THIS 
Page 22:
A = A ⋃ B + A ⋃ ~B
It’s too soon in the book to make me question reality. Please.