In my opinion this is the best written statistics text book in existence. Richard McElreath has done a brilliant job introducing probability theory and Bayesian statistics, making it very accessible with clear, coded examples. I consider this a must have for anyone looking to enter the world of Bayesian statistics. McElreath also has a great YouTube channel where he posts free lectures from a statistics course he teaches based on this text. More information about the book, including a Github link to the code used in the book, and McElreath’s blog (also great), can be found here.

Michael Bettencourt is one of the clearest thinkers and leaders in the Bayesian statistics community. He’s a freelance applied statisitician and a STAN core developer. His site has tons of useful information from video lectures to an in-progress draft of an upcoming textbook he is writing that includes several modelling examples with included code. He also has his own Patreon account that includes his podcast with Dan Simpson (incredibly helpful) and blog, among other things. The site can be found here.

This book by Andrew Gelman, Aki Vehtari and Jennifer Hill is a great resource to better understand and implement Bayesian regression, with a nice balance of theory and practical implementation. A link to a free PDF version of the book, as well as the code from the book, a podcast episode featuring the authors, and a purchase link can be found here.