Statistical Rethinking
by Richard McElreath · first published 2015
88% recommend11% neutral1% critical
Why HN recommends it
Intuition-first introduction to applied Bayesian modeling, light on math and heavy on insight, with code in R and Stan and free lecture videos; suits programmers and data people wanting to reason about models.
Summarised by Claude from recommending comments
"Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretatio
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…Algebra - A Modern Introduction (4th ed) - Poole 7. Probability: Introduction to Probability - Blitzstein, Hwang + online course https://projects.iq.harvard.edu/stat110 8. Statistics: (for Bayesian) Statistical Rethinking - A Bayesian Course with Examples in R and Stan - McElreath + online course https://www.youtube.com/playlist?list=PLDcUM9US4XdM9_N6XUUFr... Usually you'll be doing courses on #4, #5, and #6 simultaneously.
on “Ask HN: How to self-learn math?” ↗…most aspiring data scientists would be better served going with more introductory texts and really understanding them. Maybe Blitzstein and Hwang's "Introduction to Probability" and then McElreath's "Statistical Rethinking" or Wasserman's "All of Statistics" for people who need more stats. I'm not even sure what to recommend for developing good software judgment and habits. There doesn't seem to be a shortcut for that.…
on “Best Data Science Books According to the Experts” ↗If you want to learn Stan I highly recommend the book Statistical Rethinking (2nd Ed) by Richard McElreath. It’s a pedagogical masterpiece and light years away the best resource I’ve found on learning Bayesian inference.
on “Stan is a state-of-the-art platform for statistical modeling” ↗It's because most are very dry and poorly written. There are some real gems out there though. So maybe we can list some here? Here's some of my favorites: - Statistical Rethinking : Richard McElreath (also great Twitter and online lectures) - Introduction to Quantum Mechanics: Griffiths (anything he writes is good) - Classical Dynamics of Particles and System : Stephen Thornton
on “Ask HN: What do you read for general knowledge?” ↗My personal favorite is Richard McElreath's "Statistical Rethinking", which covers regression and multilevel modeling from a Bayesian approach but doesn't assume too much formal math background.
on “Ask HN: Recommend Books on Statistics” ↗…Illustrative Guide to Multivariable and Vector Calculus (Miklavcic): https://www.amazon.ca/gp/product/3030334589/ - Probability: A Graduate Course (Allan Gut): https://www.amazon.ca/gp/product/1461447070/ - Statistical Rethinking (McElreath): https://www.amazon.ca/gp/product/036713991X/ - An Introduction to Statistical Learning (James, Witten, Hastie & Tibshirani): https://www.amazon.ca/Introduction-Statistical-Learning-Appl...…
on “Ask HN: What do you read for general knowledge?” ↗I'm not so sure about that, Richard McElreath's Statistical Rethinking is an amazing resource.
on “Markov chain Monte Carlo without the bullshit (2015)” ↗…five years. But i'm interested to read the story of this fork because the practicioner-focused redis book this guy wrote is one of the best software-related texts i've read in the past 7 - 8 years. ("Statistical Rethinking" by Richard McElreath, and "The Art of SQL" by Stephane Faroult are two others that had the same impact)
on “Forking Redis: SSL/TLS and Transactions” ↗The most intuitive explanation of GPs came to me from McElreath's Statistical rethinking. What was most sticky was that was explained in terms of a thing needed for a simple regression problem that becomes hairy with continuous variable. It was an example of modeling a caffe's wait time which…
on “Gaussian Processes, not quite for dummies” ↗The 2nd edition of Statistical Rethinking by Richard McElreath came out recently and is great. It's a rare thing to want to read a statistics textbook cover to cover but I did with this and enjoyed it. The practice questions are very well designed…
on “Ask HN: What, in your opinion, are the greatest and most useful textbooks?” ↗…suggest me an advanced Bayesian Statistics book that focuses on application without sacrificing too much mathematical rigor? I am graduating in MS in Stat. I've took a Bayesian Stat course that followed Statistical Rethinking by Richard McElreath. I liked this book because the author appeals to intuition instead of mathematical rigor. I took 2 semester long statistical inference course, so I am ready for some advance material.
on “The Economist's excess deaths model” ↗A very intuitive entry into gaussian processes comes from Chapter 12 of Statistical Rethinking by Richard McElreath: He comes at it from the regression side and explains that GP's basically occur when you have continuous variables in your regression problem like ages or income instead of individual…
on “Gaussian Processes from Scratch (2019)” ↗…years with lots of very useful tools coming out of the Bayesian community (applying proper priors, multi-level modeling, inference on more complex models) and the causal inference community. McElreath's Statistical Rethinking is a great text that deals mostly with what I would consider the current state of the art in observational techniques. Highly recommended if isn't already on your bookshelf.
on “What we learned in studying the most effective founders” ↗I am partway through McElreath's "Statistical Rethinking" and I fully agree with this.
on “Introduction to Modern Statistics” ↗For Bayesian stats, "Statistical Rethinking" by McElreath is a masterpiece.
on “Introduction to Support Vector Machines in Machine Learning” ↗