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    Statistical Rethinking (2024 Edition)

    (github.com)
    193 points lnyan | 14 comments | 16 Nov 24 18:08 UTC | HN request time: 1.288s | source | bottom
    1. nextos ◴[16 Nov 24 20:40 UTC] No.42159124[source]▶
    It's a great book, but my personal opinion is that it would have benefited from an editor that recommended some small changes. The previous edition had a TOC which was barely usable because all funny jokes in chapter names like "8 Conditional Manatees". Besides, there were too many jokes embedded in some sections, which made them difficult to follow. I think some of these issues are getting addressed in the current edition.

    Nonetheless, the book is very well written and all figures and examples show great attention to detail. I found Gelman et al Regression and Other Stories better for teaching newcomers, and surprisingly insightful. Statistical Rethinking is a good choice for a second course, but perhaps too informal at that stage.

    replies(3): >>42159504 #>>42159564 #>>42161449 #
    2. blackeyeblitzar ◴[16 Nov 24 21:26 UTC] No.42159504[source]▶
    What are the prerequisites for the topics covered in this book? I feel like the lecture list is hard to understand, maybe sort of like the book’s TOC.
    replies(2): >>42159576 #>>42160985 #
    3. rscho ◴[16 Nov 24 21:35 UTC] No.42159564[source]▶
    I second that. The TOC is unusable. However, it's probably aligned with the author's intention of it being a course and not a reference book.
    replies(1): >>42160345 #
    4. rscho ◴[16 Nov 24 21:37 UTC] No.42159576[source]▶
    Honestly, I think there are very little prerequisites. I'm an MD dabbling into stats and found the book very well made as well as understandable.
    replies(1): >>42160633 #
    5. bigfudge ◴[16 Nov 24 23:10 UTC] No.42160345[source]▶
    The lectures are on YouTube and are really very good.
    replies(1): >>42162988 #
    6. NeuroCoder ◴[16 Nov 24 23:50 UTC] No.42160633{3}[source]▶
    As an MD/PhD I wish all MD researchers read this book. Heck, I wish all neuro researchers read it. If you are already established in in stats and math and your interest is just another math book to casually read or reference, this is a bad choice
    replies(1): >>42161165 #
    7. dan-robertson ◴[17 Nov 24 00:48 UTC] No.42160985[source]▶
    The original target audience was phd students in the sciences who want to do statistics to do science. So:

    - the book tries to be practical and applicable for science

    - the book assumes some amount of mathematical maturity and ability to fiddle with somewhat simple data

    - the book is not about mathematical statistics – no proving things about maximum likelihood estimators

    - the book doesn’t teach you about programming in R

    8. fn-mote ◴[17 Nov 24 01:21 UTC] No.42161165{4}[source]▶
    WHY do you think it’s bad for that background? Please!

    What if you know math but not stats? How much stats do I need to know before you think this isn’t good to browse?

    Wish I knew… I guess I’ll have to find out the hard way.

    replies(2): >>42161239 #>>42162119 #
    9. NeuroCoder ◴[17 Nov 24 01:35 UTC] No.42161239{5}[source]▶
    It's just very conversational. If you are comfortable with stats and just need a reference it can be obnoxious. I think I went through the first edition in my PhD and it was better than a stats course. But when I want a quick reference for something it is to much reading to get to the point. It might be more well organized now though.
    10. BOOSTERHIDROGEN ◴[17 Nov 24 02:18 UTC] No.42161449[source]▶
    Thank you for referencing the Gelman books. I keep struggling to understand Statistical Rethinking, which seems too advanced for me.
    replies(1): >>42168687 #
    11. crystal_revenge ◴[17 Nov 24 05:08 UTC] No.42162119{5}[source]▶
    Statistical Rethinking is an immensely practical book, and probably the best book for anyone interested in the practice of statistics.

    However, it is a bit too cautious about scaring readers away from the details of how things work. Honestly, I disagree with the parent that it's a bad book for the more mathematically inclined, since I can't think of any other book that gets you solving practical problems faster. But, if you have a strong math (or computational) background, you will be craving a deeper look under the hood.

    ET Jaynes' Probability Theory: the Logic of Science is, imho, the best book for someone who wants to really understand the theory and reasoning behind statistics and is comfortable lots of mathematical thinking.

    For a more practical (than Jaynes) but still more detailed book on statistics then I would recommend Bayesian Modeling and Computation in Python. Not quite as easy reading as Statistical Rethinking but there will be no mystery as to what's happening.

    12. fithisux ◴[17 Nov 24 09:16 UTC] No.42162988{3}[source]▶
    I second that. There is Julia material to follow along.
    13. nextos ◴[18 Nov 24 00:46 UTC] No.42168687[source]▶
    You can also try looking into The BUGS Book, A Practical Introduction to Bayesian Analysis or Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Both are excellent introductory Bayesian books. Also check this presentation out for more recommendations: https://drive.google.com/file/d/1lPePNMGMEKoaDvxiftc8hcy-rFp...
    replies(1): >>42173768 #
    14. BOOSTERHIDROGEN ◴[18 Nov 24 16:12 UTC] No.42173768{3}[source]▶
    Thanks, the presentation is indeed a well-crafted page.