Reddit mentions of Doing Bayesian Data Analysis: A Tutorial with R and BUGS

Books:

"Doing Bayesian Data Analysis" by Kruschke. The instruction is really clear and there are code examples, and a lot of the mainstays of NHST are given a Bayesian analogue, so that should have some relevance to you.

"Bayesian Data Analysis" by Gelman. This one is more rigorous (notice the obvious lack of puppies on the cover) but also very good.

Free stuff:

"Think Bayes" by our own resident Bayesian apostle, Allen Downey. This book introduces Bayesian stats from a computational perspective, meaning it lays out problems and solves them by writing Python code. Very easy to follow, free, and just a great resource.

Lecture: "Bayesian Statistics Made (As) Simple (As Possible)" again by Prof. Downey. He's a great teacher.

Tfw I'm the most knowledgeable person about statistics I know and I have read 0 of these books. Time to get reading! Although I still want to go with Doing Bayesian Data Analysis: A Tutorial with R and BUGS over Gelman et al because I want to do all the work in R. The book itself has 51 reviews on Amazon, 44 of which are 5 stars, for a mean of 4.8. That seems very good.

Saved this thead for future reference. :)

This is a really good book on Bayesian statistics, but Kruschke is coming out with a new edition in about two months with completely different code. It's going to use JAGS and STAN instead of BUGS.

This is my first time reading this page and I am quite the amateur programmer.

I am an Assistant Professor in Criminal Justice; however, my passion is quantitative methodology and understanding big data.

I had a great opportunity to spend a summer learning Bayesian at ICPSR, but to be honest some of the concepts were hard to grasp. So, I have spent the greater part of the past year learning more about maximum likelihood estimations and Bayesian modeling.

I am currently reading The BUGS Book and [Doing Bayesian Analysis] (https://www.amazon.com/Doing-Bayesian-Data-Analysis-Tutorial/dp/0123814855/ref=sr_1_fkmr1_3?s=books&ie=UTF8&qid=1519347052&sr=1-3-fkmr1&keywords=bayesian+anaylsis+bugs).

I regularly teach linear modeling at both the undergraduate and graduate level. Lately, however, I have become interested in other techniques of prediction such as nearest neighbor analysis. About a month ago, I successfully created a model predicting plant specifications with the help of [Machine Learning with R] (https://www.amazon.com/Machine-Learning-techniques-predictive-modeling/dp/1784393908/ref=sr_1_2_sspa?s=books&ie=UTF8&qid=1519347125&sr=1-2-spons&keywords=machine+learning+in+R&psc=1). Of course, this is probably elementary for many of you here but I still found the process easy to understand and now I'm planning to learn about decision trees and Naive Bayes analysis.

I was referred to this book:

http://www.amazon.com/Doing-Bayesian-Data-Analysis-Tutorial/dp/0123814855/ref=pd_bxgy_b_img_c

Hey. We can't approve this type of question. You could take it over to /r/statistics maybe.

A couple books I've looked at are Applied Bayesian Statistics and Doing Bayesian Data Analysis. Both are written at a pretty low level. The former kind of falls apart after the first few chapters, but the latter is pretty well respected (my university library had both online for free). Both cover the basics upfront but in different levels of detail. Some of the notations and derivations may be uncomfortable for you in some books (not seeing that you have taken a formal probability course and the types of distributions and procedures you use in Bayes aren't covered in intro-stats... beta, inverse gamma, MLE derivation...) so I'd try to look more at example heavy references. Be sure to specify whether you are looking for books or online references when you re-ask your question.