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Bayesian Statistical Modelling with Stan and brms (BMSB01)

1 message · Oliver Hooker

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Oliver Hooker
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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 oliver at prstats.org with any questions.