It probably goes without saying, but just in case you missed it, make sure you check out McElreath’s lectures on his text, too youtube.com/channel/UCNJK6…. He has three semester’s worth and they’re overall really great.
And plus, I also like his sand-alone lecture on “Bayesian Statistics without Frequentist Language” . It’s more conceptual than applied, but we could all probably do with a little more philosophy of statistics in our lives.
Recently, I’ve been slowly going through Krushke’s intro text sites.google.com/site/doingbaye…. Compared to McElreath, Kruschke's a touch heavier on the math and his book is organized quite differently. He also covers some great additional topics, such as Bayesian power analyses.
Still on the applied side, many of us have been waiting eagerly for the revision of Gelman and Hill’s classic text stat.columbia.edu/~gelman/arm/. The original is great in a pinch, but the code is really quite dated and Gelman’s thoughts on things like priors have since changed.
If you have a background in SEM, the Mplus team’s lectures on Bayesian SEM are quite nice (i.e., Topic 9 statmodel.com/course_materia…). Though they only show Mplus code, the big ideas are probably still worth it even if you prefer other programs.
For more informal sources, definitely check in on Gelman’s blog statmodeling.stat.columbia.edu, which sometimes has a very active comments section.
You can also find a glut of online lectures from folks on the Stan team mc-stan.org/users/document….