Bayesian data analysis
by Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin, David B. Dunson, Aki Vehtari · first published 1995
55% recommend41% neutral4% critical
Why HN recommends it
Go-to applied Bayesian text covering modeling, hierarchical models, and MCMC, with a free PDF. Suits readers with calculus and matrix algebra who want to move from basics to real Bayesian practice.
Common criticism: Some find it too technical and jargon-heavy, and hard to parse
Summarised by Claude from recommending comments
"Bayesian Data Analysis is a comprehensive treatment of the statistical analysis of data from a Bayesian perspective. Modern computational tools are emphasized, and inferences are typically obtained using computer simulations.". "The principles of Bayesian analysis are described with an emphasis on practical rather than theoretical issues, and illustrated using actual data. A variety of models are considered, including linear regression, hierarchical (random effects) models, robust models, generalized linear models and mixture models.". "Two important and unique features of this text are tho
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…vacation, to learn something new and not necessarily work-related. This year, however, I am not sure which book to pick up. I would love to see what's on your summer reading lists. My recent favourites: Bayesian Data Analysis, 3rd ed, by Gelman et al Designing Data-Intensive Applications, by Martin Kleppmann The Art of Electronics, by Horowitz & Hill
on “Ask HN: Technical Summer Reading List” ↗…written books in the field. Another old book that stands up very well is "Probability Theory: the Logic of Science" by E. T. Jaynes. "Elements of Statistical Learning" by Tibshirani is also good. "Bayesian Data Analysis" by Andrew Gelman is another great read. "Deep Learning" by Ian Goodfellow and Yoshua Bengio is useful for getting caught up with recent advances in that field.
on “Ask HN: What maths are critical to pursuing ML/AI?” ↗…starting place is: 3.) Lehmann, E. (2004). "Elements of Large-Sample Theory" Springer. Those are all frequentist books. You should also read something Bayesian: 4.) Gelman, A. et al. (2003). "Bayesian Data Analysis" Chapman & Hall/CRC. and you should start to read about Bayesian computation: 5.) Robert, C. and Casella, G. (2005). "Monte Carlo Statistical Methods" Springer. On the probability front, a…
on “Learning About Statistical Learning” ↗…Logic of Science" by E. T. Jaynes http://www.med.mcgill.ca/epidemiology/hanley/bios601/Gaussia... "Elements of Statistical Learning" by Tibshirani https://web.stanford.edu/~hastie/Papers/ESLII.pdf "Bayesian Data Analysis" by Andrew Gelman http://hbanaszak.mjr.uw.edu.pl/TempTxt/(Chapman%20&%20Hall_C...
on “Ask HN: What maths are critical to pursuing ML/AI?” ↗…think to be helpful you have to present case studies of reproducing research but with metric optimization swapped out for a holistic variety of metrics plus qualitative checking. I recommend the books Bayesian Data Analysis by Gelman et al and Data Analysis Using Regression and Multilevel/Hierarchical Models by Gelman and Hill if you want to read good accounts of doing this in practice with real data sets. There’s definitely…
on “The problem with metrics is a big problem for AI” ↗You could read Gilbert Strang's Linear Algebra books, there are also video lectures available... Gelman's Bayesian Data Analysis is a good statistical intro with a Bayesian bend, maybe Freedman's Statistical models, read some general machine learning intro book, such as Alpaydin or Bishop for a more probabilistic view. For NLP…
on “Ask HN: good math books” ↗…call, you have it in Stan language. If you're interested in this, Gelman has two great books. Gelman & Hill's Hierarchical Models[2] which is applied and geared towards social science researchers, and Bayesian Data Analysis[3]. [1] http://andrewgelman.com/ [2] http://www.stat.columbia.edu/~gelman/arm/ [3] http://www.stat.columbia.edu/~gelman/book/
on “Stan: a probabilistic programming language” ↗…Regression by Gelman Introduction/Elements of Statistical Learning by Jerome Friedman. I recommend reading the Introduction and using the bigger book as a reference material when tackling a problem. Bayesian Data Analysis, 3rd edition by Gelman. You need calc 1 & 2 and matrix algebra somewhere along the way. Lots of papers, googling and doing. That's when you got the basics covered. You start being "operational" after…
on “Learn the basics of data science with these books” ↗Bayesian Data Analysis by Andrew Gelman http://www.stat.columbia.edu/~gelman/book/
on “Interview on ”Bayesian Statistics the Fun Way”” ↗Gelman's (one of the authors behind Stan) Bayesian Data Analysis is considered to be one of the best resources on this subject, so I suggest giving that a read. It's not the easiest book to work through though.
on “Foundations of Probabilistic Programming” ↗Bayesian Data Analysis by Gelman et al. There's a free PDF available on Gelman's website [1]. Definitely application-focused, and the authors are a who's-who of contemporary Bayesian statisticians. [1] http://www.stat.columbia.edu/~gelman/book/
on “The Economist's excess deaths model” ↗Check out Bayesian Data Analysis by Gelman too. BDA and MLAPP are my go to resources for anything Bayesian.
on “Ask HN: Good resources to learn Variational Bayes method” ↗…direct you to the best place to start, it would probably be in one of these books: E.T. Jaynes, "Probability Theory: The Logic of Science" Judea Pearl, "Probabilistic Reasoning in Intelligent Systems" Bayesian Data Analysis, 2nd ed by Gelman et. al
on “What's luck got to do with it? The math of gambling” ↗Actually, one of the best ways to learn applied bayesian stats, used by most machine learning researchers I've asked about this, is to read Gelman's books on bayesian data analysis and hierarchical/multilevel modeling.
For frequentist statistics, I'd recommend Statistical Inference by Casella and Berger. For Bayesian statistics, I'd recommend Bayesian Data Analysis by Gelman et al.
on “Good statistic book for CS post-graduate?” ↗