Is there any (probably online in these days) course you can recommend to learn more about Bayesian inference and RStan? I have some basic understanding of coding simple models in Stan (e.g., linear regressions) and want to learn more about it. A good course may well cost something.
Any recommendation is welcome.
All the below are free, though not specific to the rstan interface:
Ben Goodrich posts his lectures for graduate-level Bayesian statistics online: https://www.youtube.com/playlist?list=PLSZp9QshJ8wxZ0Gc96WhiIU0NoRL_R8ov
Richard McElreath has posted his lectures, which follow his textbook: https://www.youtube.com/playlist?list=PLDcUM9US4XdNM4Edgs7weiyIguLSToZRI
Ben Lambert, which relates to his textbook: https://www.youtube.com/playlist?list=PLwJRxp3blEvZ8AKMXOy0fc0cqT61GsKCG
I’m sure there are more.
Thank you, that’s very helpful.
I was just wondering if there is nothing more applied than lectures on platforms like datacamp, coursera etc.?
“more applied” - what do you mean?
Bayesian Inference is a general framework. you need to understand the theory and principles before you can put them into practice. the Stan case studies provide an excellent introduction - my fave is Bob Carpenter’s Hierarchical Partial Pooling for Repeated Binary Trials - perhaps I’m biased -
there are domain-specific examples of Stan models - again, in order to use them, you should understand the what these models do and follow the Bayesian workflow of model checking and model comparison.
p.s. sounds like you’ve got RStan already - but if you don’t and run into install problems, consider CmdStanR - http://mc-stan.org/cmdstanr/