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Bayesian Statistical Modelling with Stan and brms

Oliver Hooker 
Develop advanced Bayesian modelling skills in our live online course *Bayesian
Statistical Modelling with Stan and brms (BMSB01)*.

This hands-on workshop provides a comprehensive introduction to Bayesian
data analysis using *Stan* via the *brms*package in R. Participants learn
both the theoretical foundations of Bayesian inference and practical
workflows for fitting and interpreting Bayesian models with real datasets.

The course covers the full Bayesian modelling workflow, including how to
define priors, fit models, evaluate convergence, and interpret posterior
distributions.

*The course covers:*

   -

   Foundations of Bayesian reasoning and statistical inference
   -

   Fitting Bayesian regression models in R using the *brms* interface to
   Stan
   -

   Understanding likelihoods, priors, and posterior distributions
   -

   Model comparison and Bayesian diagnostics
   -

   Interpreting MCMC output (trace plots, R-hat, effective sample size)
   -

   Extending models to generalized linear and hierarchical models

Through practical coding exercises, participants gain experience
implementing Bayesian models and interpreting results in a modern
statistical workflow.

Delivered live online, the course allows direct interaction with the
instructor, opportunities to ask questions, and discussion of your own
modelling challenges. All participants receive course materials, example
code, and post-course support.

*Course details*
Dates: 5?7 May 2026
Duration: 3 days (approximately 6 hours per day)
Format: Live online
Fee: ?400

This course is suitable for postgraduate students, researchers, and
analysts who want to apply Bayesian methods to real datasets using R and
Stan.

Full details and registration:
https://prstats.org/course/bayesian-statistical-modelling-with-stan-and-brms-bmsb01/
Email oliver at prstats.org with any questions