To be honest my understanding of math and statistics is far too basic for me to really approach this guy's work, in all likelihood. I have read some papers in this space, like the original TrueSkill paper[1], and I found them utterly impenetrable. I'm sure with sufficient practice I could learn but there are so many things to spend time on. I love the concepts and I do think that they are fascinating tools for modeling reality. I'm glad other people are developing them.

[1]: https://www.microsoft.com/en-us/research/wp-content/uploads/...

A pathway to understanding TrueSkill could be first getting an understanding of factor graphs and Minka's expectation propagation algorithm [1][2]. I only have a superficial understanding of those things, but I think I've got an understanding of things that are close enough to give some idea of a pathway:

To understand Minka's expectation propagation algorithm you might first need to get a little intuition about assumed density filtering. One way to understand assumed density filtering could be to read a few tutorials about hidden Markov models [3] or Kalman filters and try to get a feel for why and when and how people might want to approximate posterior probability distributions. It might be hard to build enough intuition without trying to actually apply the things (implement the algorithms) or prove the theory yourself, and then try to come up with your own ideas for how to improve the algorithms.

I completely agree that there are basically infinitely more things to learn than available lifetime. It helps a lot to have a concrete application or goal in mind: then you can focus on learning the tools and theory that move you closer to the goal, rather than learning bits and pieces of unrelated knowledge that don't connect together in a useful way.

[1] https://tminka.github.io/papers/ep/roadmap.html

[2] Minka's EP slide deck from his PhD defense https://tminka.github.io/papers/ep/defense.pdf

[3] Rabiner wrote a famous HMM tutorial https://courses.physics.illinois.edu/ece417/fa2017/rabiner89...

I can almost guarantee you that if you're on Hacker News, you have the prerequisites needed for Judea Pearl.

I read Causality which I understand is the more technical of their books, and everything was presented surprisingly intuitively. Sure, I had to go over some things twice, but that's to be expected when you learn something new.

If you're worried, start with one of the more pop-aimed books? You'll be fine.

(Pearl did change the way I look at causality and correlation, fundamentally for the better, so I do strongly recommend getting familiar with it. I also liked Willful Ignorance which is sort of one the same theme but also not and takes a wider approach.

I agree with this. Causality doesn't have much in the way of advanced math or statistics, but there is a fairly large volume of material to go through, which is its own challenge.

A lightweight introduction to Pearl's ideas is the epilog of his book, which is also his Turing award lecture. Here's a pdf scan, there's also video of him giving this lecture up on the internet if you prefer: http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf

here's an arbitrary review of Pearl's recent book, the book of why: https://www.ams.org/journals/notices/201907/rnoti-p1093.pdf