Imperialstan

[This article was first published on Gianluca Baio's blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Despite the map here, I’m not going to talk about yet another fraction of the former Soviet Empire which is taken the form of a people’s republic, possibly with witty British Ambassadors.

In fact, I’m going to talk about the Stan workshop that I have be to, earlier today, which was held at Imperial College. My friend Lea organised it and Mike Betancourt (who’s actually in my department at UCL) run the show (brilliantly, it has to be said).

In the morning, Mike gave a brief overview of MCMC and introduced the basics of Hamiltonian Monte Carlo (I think this by Neil Radford is just a great introduction to the topic). Then in the afternoon he concentrated on Stan and rstan in particular (which, unsurprisingly, is the R interface to the actual HMC engine).

I think this was kind of the first of a potential series of similar talks/workshops and I found it very useful. Of course it’s always difficult to strike a balance between how in depth you want to go with the theory and the examples, so for instance, I think a little more on the actual NUTS algorithm would have been helpful $-$ but as I said, I know full well how hard it is to do this, so well done, Mike!

To leave a comment for the author, please follow the link and comment on their blog: Gianluca Baio's blog.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)