Hi everyone,
I hope this message finds you well.
I want to share that I will be running an introductory course on Bayesian Inference and Modelling in Stan. It will be a 4-day in-person programme designed for academics and professionals working across industry and research wanting to develop an understanding of Bayesian methods.
The course will be held in-person at the University College London (Bloomsbury Campus):
- Dates: 8-11 June 2026
- Location: Charles Bell House G04 Seminar Room 2, 43-45 Foley Street, Fitzrovia, London, W1W 7TS, United Kingdom. [Map]
- Format: Lectures + hands-on computer seminars (with live walkthroughs and breakdown of coding etiquettes in R and Stan)
For more information about the course and registration, please click [HERE]
For information about me, please click on [UCL Profile Page] [LinkedIn]
Alternatively, see full course details below.
Course title: Introduction to Bayesian Inference and Modelling
Overview
This 4 day course introduces academics and professional data analysts to Bayesian inference, using the Stan interface in R. The atmosphere of the workshop will be friendly and supportive, with the goal of teaching the basics of Bayesian inference in Stan for academics and professionals alike from diverse backgrounds ranging from industry to research fields such as population health, social sciences, disaster risk reduction, and many more.
We will show participants how one can develop and compile Stan scripts for Bayesian inference through RStudio to perform basic parameter estimation, as well as a wide range of regression-based techniques from the simplest univariate linear models to more advanced multivariate spatial risk models.
Participants will leave the course with a clear understanding of the Bayesian approach to data analysis and inference, and its applications in a range of fields.
Who this course is for
The course is aimed at anyone who wants to develop an understanding of Bayesian methods, whether in academia or professional research settings. Participants must be familiar with statistics up to and including multiple linear regression. Some prior experience of using R software is also necessary.
Course content
The course will be structured as follows:
- Day 1: Introduction to Probability Distributions
- Day 2: Introduction to Bayesian Inference
- Day 3: Bayesian Generalised Linear Models
- Day 4: Spatial Risk Models
Teaching and structure
The course will consist of 4 lectures and 4 computer seminar sessions supported with live walkthrough coding demonstrations. Participants may wish to refresh their knowledge of basic statistics/regression and R coding prior to the course.
Certificates
Participants will be issued with a certificate of participation upon completion of the course.
Learning outcomes
By the end of this course you will:
- Have both a foundational and advanced understanding of key principles of statistical modelling within a Bayesian framework
- Be able to perform inferential statistics on spatial and non-spatial data to carry out hypothesis testing for evidence-based research using the diverse types of regression-based models from a Bayesian framework
- Be able to perform spatial risk prediction for areal data as well as quantify levels of uncertainty using exceedance probabilities
- Have acquired new programming language skills in Stan (interfaced with RStudio).