# Statistical Rethinking Book [and other resources]

Today Twitter pointed me to a very interesting and practical book about Bayesian Statistics with examples in R and STAN.

Statistical Rethinking 2nd edition page now lists code conversions for:

— Richard McElreath 🦔 (@rlmcelreath) August 1, 2020

* raw Stan+tidyverse

* brms+tidyverse

* PyMC3

* Tensorflow Probability

* Julia & Turing

I know other conversions in the works. If I have missed something, please let me know. https://t.co/jyp2mxBKgC pic.twitter.com/fM5ZBj1p29

The first two chapters are available free of charge from the book homepage. If you want a gentle and practical approach to Bayesian statistics I think this book could be a good first step.

In the first chapter of the book, there a simple but “useful” chart showing the usual prodecure for dealing with data. Obviously, this is something could change if we start to *rethink* our statistical approaches.

Another interesting twitter about bayesian methods in Machine Learning shows a really nice summary. I will copy here the first page of it. Use the twitt below for the complete summary as well as the links to the videos of the lecture.

Beautiful overview of Bayesian Methods in ML by @shakir_za at #MLSS2020. Left me pondering about many things beyond Bayesian Inference. Thank you Shakir🙏

— Robert Lange (@RobertTLange) July 10, 2020

Quote of the day: “The cyclist, not the cycle, steers.“🚴♀️

🎤 P-I: https://t.co/yWR4BSJlw5

🎤 P-II: https://t.co/ipwwYCgGC4 pic.twitter.com/qoeD2L1YIr

To conclude the post I will provide links to two well-known books for the more traditional *frequentist* statistics:

- The well known book by Andy Field,
*Discovering Statistics using R* - from Daniel Navarro
*Learning Statitics with R*.

The last one is available free of charge here