This is an intensive, two day course introducing the use of RStan for Bayesian computation. The course will be a mixture of lectures and computer practicals. The main focus will be on the specification of models using the Stan language and on the practicalities of generating samples from the posterior distribution and diagnosing convergence.

Participants should be familiar with basic Probability and Statistics including common distributions and regression. Basic R programming, is also required, i.e. writing loops and functions. We do not expect you to have experience with Bayesian Inference or Stan, but some knowledge of the former will be helpful.

COURSE OUTLINE:

• Introduction to Bayesian inference: A brief overview of the main ideas behind Bayesian inference.
• Markov chain Monte Carlo methods: A brief overview of Markov chain Monte Carlo methods for Bayesian computation and Hamiltonian Monte Carlo.
• The Stan language: An outline of the main components of a Stan program.
• Using RStan: A guide to the use of the R interface to Stan.
• Examples: Including linear regression, Poisson regression and hierarchical model

If you are interested in this course you can also contact us directly for information about how you can study at our offices for a lower price.

For more information please go to our website at Jumpingrivers.com