7 ms·
Bayesian Data Analysis, Third Edition [pdf]
- whatok 7y agoThe book is also available on Prof Gelman's department site which I would probably link to instead: http://www.stat.columbia.edu/~gelman/book/ http://www.stat.columbia.edu/~gelman/book/
- ajaalto 7y agoThe first link points to Aki Vehtari's personal page. He is one of the co-authors.
- malshe 7y agoThis is Aki Vehtari's website who is a coauthor of this book and I got the link from his Github page: https://github.com/avehtari/BDA_course_Aalto https://github.com/avehtari/BDA_course_Aalto
- petulla 7y agoGelman et al also updated Regression and Other Stories's example page https://avehtari.github.io/ROS-Examples/ https://avehtari.github.io/ROS-Examples/
- deleted 7y ago[deleted]
- deleted 7y ago[deleted]
- metakermit 7y agoIf someone wants a more interactive companion-book targeted more towards Python developers, check out "Probabilistic Programming & Bayesian Methods for Hackers": http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/ http://camdavidsonpilon.github.io/Probabilistic-Programming-... Relevant quote: > "I ... read this book ... I like it!" - Andrew Gelman
- faizshah 7y agoThanks, I was looking for a practical book like this!
- xitrium 7y agoThis is weirdly deliberately misquoted, maybe as a joke? full: https://statmodeling.stat.columbia.edu/2013/07/21/bayes-related/ https://statmodeling.stat.columbia.edu/2013/07/21/bayes-rela...
- rahimnathwani 7y agoIt says above that quote: "These are satirical, but real"
- mwexler 7y agoAnother good one to read is Statistical Rethinking via https://xcelab.net/rm/statistical-rethinking/ https://xcelab.net/rm/statistical-rethinking/. A bit easier to understand than Gelman's book, but together, these give you an amazing foundation in modern bayesian analysis. Cam's book, mentioned also in the comments, is also wonderful.
- 0xdeadbeefbabe 7y agoWhat are the chances a good foundation in modern bayesian analysis will pay off? I feel like I won't be able to answer with satisfaction till I have a good foundation...
- ssivark 7y agoI think a good conceptual foundation is the difference between someone who throws buzzword solutions at a problem to see what sticks, and someone who can make a good tweak to get working a standard-ish idea that doesn’t quite work out of the box. Without understanding these concepts it is easy to get stuck spinning in loops on a complicated problem — where tweaking to improve one thing messes with another thing. I won’t claim Bayesian is the only conceptual framework, but I found it particularly intuitive and straightforward — and gives you a lot of flexibility. Refer an earlier discussion on Bayesian approaches a few days ago.
- rhymer 7y agoSecond this. Richard is a great lecturer. Highly recommend his lecture recordings. His winter 2019 lecture videos and materials can be found here: https://github.com/rmcelreath/statrethinking_winter2019 https://github.com/rmcelreath/statrethinking_winter2019
- oarabbus_ 7y agoAt the risk of sounding quite silly, how do people read these textbooks? Do people (who are not in graduate studies) actually work through entire books, or just particular chapters? I grinded through textbooks during my graduate studies, but I had to, in order to complete the HW and pass the courses. But since joining industry I've not been able to actually work through a textbook - when I try to attempt the problems, I'll find a couple have passed and only one or two problems have been completed - I simply find it a challenge to find the time to work through book exercises.
- lorenzfx 7y agoHas somebody read the book and can let us know how it compares to similar books? Would you recommend it as an introduction to topic? I've always heard that it's a bit on the dry side of things, but haven't actually read it myself.
- martingoodson 7y agoIt's a great book if you want to understand bayesian modeling in detail. Its not 'dry' as in boring - it's an interesting read. If you want something less technical then read Gelman and Hill 'Data Analysis Using Regression and Multilevel/Hierarchical Models', which is also great. More for scientists than statisticians, I'd say.
- anthony_doan 7y ago> Would you recommend it as an introduction to topic? No. I would not recommend it unless you have a strong foundation in statistic. If you want an introduction, I like "Doing Bayesian Data Analysis: A Tutorial with R and BUGS" by John K. Kruschke. It's basically the Bayesian intro statistic version of Wackerly's statistic book (frequentist).
- cambalache 7y agoFor the unaware I also recommend prof Gelman blog. https://statmodeling.stat.columbia.edu/ https://statmodeling.stat.columbia.edu/
- clircle 7y agoGreat book loaded with practical information. I'll also recommend Christian Robert's The Bayesian Choice for the more math-y decision theory crowd.
- kgwgk 7y agoFrom the same author but more practical: https://link.springer.com/book/10.1007/978-1-4614-8687-9 https://link.springer.com/book/10.1007/978-1-4614-8687-9
- rintakumpu 7y agoLecture videos to go with it https://aalto.cloud.panopto.eu/Panopto/Pages/Sessions/List.aspx#folderID=%22f0ec3a25-9e23-4935-873b-a9f401646812%22 https://aalto.cloud.panopto.eu/Panopto/Pages/Sessions/List.a....
- noctilux 7y agoThis is a _fantastic_ book. If anyone's worried it's too technical, I'd say that it's not as dry as it might look at first glance. There's lots of practical advice and there's actually not that much heavy maths.
- anthony_doan 7y ago> there's actually not that much heavy maths. Uh... sure if you know measure theory. The later chapter especially in Dirichlet chapters assume you know measure theory.
- ssequeira 7y agoThere's little that can be said about Dirichlet processes without measure theory; fundamentally speaking they are random measures. That said, the vast majority of the book presupposes no measure theory.
- stenecdote 6y agoI don't think this is true having read some of the later chapters and not knowing measure theory. And if you don't trust me, Gelman doesn't know measure theory either (https://statmodeling.stat.columbia.edu/2008/01/14/what_to_learn_i/ https://statmodeling.stat.columbia.edu/2008/01/14/what_to_le...) and he wrote the book...
- anthony_doan 7y agoThis is the bible for Bayesian statistic. It's phd imo or a senior level grad student. I was hoping he updated the other book for Hierarchical modeling with rStan.
- deleted 7y ago[deleted]