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Bayesian Modelling Using R & INLA - Dr Virgilio Gómez-Rubio

Oliver Hooker 
Advance your statistical modelling skills with our live online course *Bayesian
Modelling Using R & INLA (BMIN04)*.

https://prstats.org/course/bayesian-modelling-using-r-inla-bmin04/

Instructor - Dr Virgilio G?mez-Rubio, a lead expert in the application of
INLA and the author of 'Bayesian Inference with INLA'.

This applied training provides a practical introduction to Bayesian
inference using the Integrated Nested Laplace Approximation (INLA)
framework in R. Participants will learn both conceptual foundations and
hands-on implementation of Bayesian models for complex data structures,
with workflows suitable for spatial, temporal, and hierarchical analyses.

The course covers:

   -

   Bayesian fundamentals and interpretation
   -

   Specification and fitting of Bayesian models in R using INLA
   -

   Model selection, comparison, and diagnostics
   -

   Handling spatial, temporal, and spatio-temporal data
   -

   Hierarchical and mixed-effects modelling in a Bayesian framework
   -

   Practical strategies for reproducible Bayesian workflows

Delivered live online, this course offers direct interaction with the
instructor, opportunities to ask questions about your own data, and
structured guidance through practical examples. All participants receive
course materials, example code, and post-course support.

*Course details*
Dates: 4-8 May 2026
Duration: 5 days (approximately 7 hours per day)
Format: Live online
Fee: ?500

This course is suitable for postgraduate students, researchers, analysts,
and professionals interested in applying Bayesian methods to real data
using R and INLA.

Full details and registration:
https://prstats.org/course/bayesian-modelling-using-r-inla-bmin04/
Email oliver at prstats.org with any questions