Recommendations for intro to Bayes R tutorials/material/books for complete beginners?

Hi everyone,

Does anyone have any recommendations for very very beginner-oriented Bayes tutorials (in R, ideally)?

I'm trying to better learn Bayesian statistics in R, but lots of the material I see starts spewing names of distributions and other statistics-related things that, frankly, I don't really understand right now.



Kind of confused by your categorization. You have machine learning as a category but you're also asking about Bayes, which is an overlapping but also completely different topic.

Anyway, I think Doing Bayesian Data Analysis by John Kruschke is a must read.

Along with Statistical Rethinking by Richard McElreath. The latter book is good for understanding Bayes, but I personally didn't find it a good choice for trying to do Bayesian modelling with real datasets because most of the code in the book is used for the rethinking package, created by the author to drive home important concepts. But it's not the type of scripts you would use in a real world.

This contrasts with the book by Kruschke, which is a lot more comprehensive and covers several topics, especially in experimental settings, and it comes with plenty of applicable scripts. Overall, I'd recommend both books, but I'd recommend Kruschke first and then McElreath to solidy concepts.

However, both books assume that you already have some knowledge about statistics in general (not R, they teach you to use R, especially Kruschke)


The category is "Machine Learning and Modeling" -- that's why I made it the category that it is. If a mod feels that it's better described as something else, then by all means...


But thanks for the reply! I'll certainly check those out.

Hi Evan,

I don't think it's a good idea to study Bayesian statistics before having studied Probability, and being comfortable with Gaussian distributions, Binomial distributions, Beta distributions, etc. You can definitely study Bayesian statistics before studying Frequentist statistics (though it's uncommon), but since Bayesian statistics makes an heavy use of probabilistic concepts, I'd say Probability is a mandatory requirement to understand it well. However, not everyone agrees with my point of view: you may find this book useful

Ps sorry but it's in Python.

1 Like


Kruschke's book is very good too

It belongs here


This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.

If you have a query related to it or one of the replies, start a new topic and refer back with a link.