Online | July 22nd, 2021,

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Course Details

Course Outline
Learning Outcomes
Materials
Prior Knowledge

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 models.

Learning Outcomes

By the end of the two days participants will…

have a good understanding of Bayesian inference/MCMC methods
understand how these methods can be applied to their own data
understand how to apply these methods to their own data using R’s interface to Stan, RStan.

Materials

Prior Knowledge

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.

Attendee Feedback

“Practicals well were structured and demonstrated lots of the capabilities. Notes had plenty of details on the basics of the language and were a useful reference guide.”
- “Jack is a very competent tutor, his presentation was very well paced and was able to resolve issues and confusions quickly.”
- “I really liked the pace and the level of detail in explaining the background on day 1. The friendly pace and care on explaining every bit that is being shown is excellent to make someone at a beginner level like myself appreciate the course.”

Training Course Details

Introduction to Bayesian Inference using RStan

Course Level: Intermediate