[HN Gopher] Bayesian Data Analysis, Third Edition [pdf]
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       Bayesian Data Analysis, Third Edition [pdf]
        
       Author : malshe
       Score  : 277 points
       Date   : 2020-05-06 14:30 UTC (8 hours ago)
        
 (HTM) web link (users.aalto.fi)
 (TXT) w3m dump (users.aalto.fi)
        
       | metakermit wrote:
       | If 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-...
       | 
       | Relevant quote:
       | 
       | > "I ... read this book ... I like it!" - Andrew Gelman
        
       | cambalache wrote:
       | For the unaware I also recommend prof Gelman blog.
       | https://statmodeling.stat.columbia.edu/
        
       | clircle wrote:
       | Great book loaded with practical information. I'll also recommend
       | Christian Robert's The Bayesian Choice for the more math-y
       | decision theory crowd.
        
       | whatok wrote:
       | The book is also available on Prof Gelman's department site which
       | I would probably link to instead:
       | 
       | http://www.stat.columbia.edu/~gelman/book/
        
         | malshe wrote:
         | This 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
        
         | ajaalto wrote:
         | The first link points to Aki Vehtari's personal page. He is one
         | of the co-authors.
        
           | malshe wrote:
           | Thanks for clarifying this! I did not realize the domain name
           | will cause skepticism :)
        
           | whatok wrote:
           | Ah my mistake. Didn't recognize the domain.
        
           | [deleted]
        
       | petulla wrote:
       | Gelman et al also updated Regression and Other Stories's example
       | page https://avehtari.github.io/ROS-Examples/
        
       | rintakumpu wrote:
       | Lecture videos to go with it
       | https://aalto.cloud.panopto.eu/Panopto/Pages/Sessions/List.a....
        
       | [deleted]
        
       | mwexler wrote:
       | Another good one to read is Statistical Rethinking via
       | 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 wrote:
         | What 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 wrote:
           | I 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 wrote:
         | Second 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
        
         | oarabbus_ wrote:
         | At 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.
        
           | Barrin92 wrote:
           | >At the risk of sounding quite silly, how do people read
           | these textbooks
           | 
           | literally just front to back. I did maths in university and
           | when I started to work for a few years I didn't get too do
           | much of it so I just got into the habit to put half an hour a
           | day aside to work through whatever books I find interesting.
           | 
           | I actually enjoy it much more now than I did in uni given
           | that I can do it at my own pace now and for fun.
        
       | lorenzfx wrote:
       | Has 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 wrote:
         | It'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.
        
       | [deleted]
        
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       (page generated 2020-05-06 23:00 UTC)