Bayesian Statistical Modelling with Stan and brms (BMSB01) <https://prstats.org/course/bayesian-statistical-modelling-with-stan-and-brms-bmsb01/>
Bayesian Statistical Modelling with Stan and brms is an advanced R course for researchers covering Bayesian model building, diagnostics, and interpretation using Stan and brms. *Advance Your Statistical Expertise with Bayesian Modelling in R* *Bayesian Statistical Modelling with Stan and brms (BMSB01)* https://prstats.org/course/bayesian-statistical-modelling-with-stan-and-brms-bmsb01/ *Next Offering: *Live online *5–7 May 2026* (UK/GMT+1) Take your data analysis skills to the next level with this comprehensive three-day live workshop designed for empirical researchers who want to confidently apply Bayesian statistical methods using modern computational tools in R. Delivered online with recorded sessions, this course offers both *conceptual foundations* and *hands-on practice* with real datasets and workflows. *What You Will Learn:* • Core principles of Bayesian inference and how they differ from classical approaches. • How to apply Bayesian reasoning and Bayes’ rule to real data problems. • Practical model building using *Stan* and the *brms* R package, leveraging Bayesian regression, generalized models, and hierarchical structures. • Model diagnostics, prior specification, posterior inference, and model comparison with tools like WAIC and LOO. • Extensions to robust regression, heteroskedastic modeling, logistic and count data models, and Bayesian mixed effects models. *Format & Features:* • *Duration:* 3 days (6 hours per day), live online with recordings available. • *Interactive learning:* Combines lecture, coding exercises, and guided discussions. • *Materials included:* Code, datasets, and slides provided to all participants. • *Support:* Continued email support for 30 days post-course. • *Prerequisites:* Familiarity with fitting linear or generalized linear models in R recommended. *Who Should Attend:* This course is ideal for researchers in the social, behavioural, biological, and applied sciences who want to deepen their understanding of Bayesian inference and learn to use *Stan*—a state-of-the-art probabilistic programming language—with the user-friendly *brms* interface. Email [email protected] with any questions. -- Oliver Hooker PhD. PR stats [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
