Dr. Doug Leasure of Real Good Research and the University of Oxford will teach a couple of intensive two-day workshops on the basics of Bayesian statistics to students of the Grand Union Doctoral Training Partnership. One course was taught alongside Dr. Michael Chong of the University of Oxford at the London School of Economics on February 5-6, 2026 and the other will be taught at the University of Oxford on June 10-11, 2026.
Course materials are available from our GitHub here.
Bayesian statistics is a powerful and flexible approach for statistical inference that offers a range of benefits over traditional approaches for making predictions, particularly in data scarce contexts, and for designing bespoke analyses for your specific dataset and application. We’ve used Bayesian statistics for applications ranging from forecasting fish populations using satellite remote sensing (Leasure et al. 2019) to mapping human population sizes for every 100-m grid cell in countries that have not conducted a census in a while (Leasure et al. 2020, Boo et al. 2022). The course will be taught using 100% free and open source software including R and Stan. Stan is software for Bayesian programming that has a huge supportive community with lots of great learning resources and discussion forums to support continued learning.
The full course advert is below. Reach out to info@realgoodresearch.com if you are interested in attending the course or organising a similar course at your institution.
This two-day course provides a practical and accessible introduction to Bayesian statistics for applied research in any field. Students will benefit from a combination of lectures and discussion to explore fundamental concepts unlocking the potential to design bespoke statistical analyses based on your data and hypotheses as well as practical exercises to gain hands-on experience implementing Bayesian models using free and open-source software. The course is designed as a springboard to overcome the steepest part of the Bayesian learning curve with an immersive two-day deep-dive.
Students can expect to gain a working understanding of Bayes theorem (i.e. likelihoods, priors, and posterior probability distributions) and implementation of Markov Chain Monte Carlo for Bayesian model fitting. The course will cover common probability distributions and the know-how to choose between them when designing bespoke models as well as a solid foundation in model validation techniques for high quality research. These concepts will be reinforced through practical lab exercises to gain experience implementing Bayesian models using R and Stan software. Students will be challenged through interactive group discussions to formalise their own mental models (i.e. hypotheses) into bespoke statistical models that can be used to confront these models with data.
Students will have the opportunity to schedule a follow-up Bayesian surgery appointment (30 mins in-person or remote) for one-to-one engagement (or small groups, as preferred) with the course instructor to answer burning questions that remain and/or to troubleshoot technical challenges related to their own research applications.
Pre-requisites: Students will need good programming skills in R and a basic understanding of linear regression to be successful in this course.
Course dates:
February 5-6, 2026 (10.00-17.00) @ London School of Economics
June 10-11, 2026 (10.00-17.00) @ University of Oxford
Surgery appointments (in-person at Oxford or remote via Teams):
* February 13, 2026 (13.00-17.00)
* June 19, 2026 (13.00-17.00)
* Additional dates/times may be offered depending on demand