Advanced Bayesian Modelling with R-INLA — 5 Days Intensive
*Bayesian Modelling using R-INLA (BMIN03)* is a comprehensive, live-online
course that equips you with the theory and practical skills to build, fit,
and interpret Bayesian models using the power of the R-INLA package.
Perfect for applied researchers, analysts, and data scientists working in
R, this five-day course covers everything from foundational Bayesian
inference to advanced latent-effect modelling.
What You Will Learn
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Core principles of *Bayesian inference* and how the INLA methodology
compares with classical MCMC-based approaches.
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How to *fit generalized and mixed models in R-INLA*, including linear,
generalized, multilevel, time-series, and spatial models.
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How to *define custom priors and latent effects*, even those not
provided out-of-the-box — giving you flexibility for complex or novel
modelling tasks.
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How to *interpret INLA output*, extract posterior summaries, and apply
Bayesian modelling with confidence.
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Hands-on experience working with real data and the option to explore
your own datasets, under guidance.
Course Format & Structure
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*Duration:* 5 days — 7 hours per day.
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*Format:* Live online sessions, recorded for later access so you can
learn flexibly.
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*Interactive:* Each day blends lectures with practical coding exercises
and Q&A — including sessions for participants to work on their own data.
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*Support:* Complete course materials (slides, code, datasets), and
30-day post-course support for follow-up questions or problems.
Who Should Attend
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Researchers, analysts, or data scientists already using R who want to
expand their toolkit with Bayesian methods and latent-effect modelling.
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Those familiar with statistics and data analysis (e.g., linear or mixed
models) but new to Bayesian inference or INLA.
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Anyone needing flexible, efficient Bayesian modelling for complex data
structures — including hierarchical, time-series, and spatial data.
*Next course date:* 23–27 February 2026 (live online)
*Course fee:* £500
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Oliver Hooker PhD.
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