Bayesian Modelling Using R-INLA (BMIN04)

https://prstats.org/course/bayesian-modelling-using-r-inla-bmin04/
Delivered by Dr. Virgilio Gómez-Rubio, author of Bayesian Inference with INLA and an internationally recognised expert in Bayesian statistics and spatial modelling.
Learn how to build fast, flexible Bayesian models using R-INLA, a powerful alternative to traditional MCMC methods.
Bayesian modelling has become an essential tool across ecology, epidemiology, environmental science, and many other research disciplines. This hands-on course introduces Integrated Nested Laplace Approximation (INLA), an efficient framework for fitting complex Bayesian models in R. You'll learn how to build, interpret, and compare Bayesian models while gaining practical experience with real-world datasets.
What you'll gain
- A strong understanding of Bayesian inference and prior specification
- Practical experience fitting Bayesian models using R-INLA
- Skills to build hierarchical, spatial, and spatio-temporal models
- Understanding of model comparison and uncertainty quantification
- Confidence in interpreting posterior distributions and Bayesian model outputs
Course format
- Live, instructor-led online training
- Hands-on coding with real-world datasets
- Interactive practical exercises throughout
- Strong focus on applied, research-ready workflows
Who is this course for?
- Statisticians and data scientists
- Ecologists, epidemiologists, and environmental scientists
- Researchers working with complex or hierarchical datasets
- PhD students and quantitative researchers
- Anyone interested in modern Bayesian modelling using R
Why take this course?
Bayesian methods provide a flexible framework for analysing complex datasets while explicitly accounting for uncertainty. However, traditional Markov Chain Monte Carlo (MCMC) approaches can be computationally demanding, particularly for large or complex models.
R-INLA offers a fast and practical alternative, allowing researchers to fit sophisticated Bayesian models efficiently without sacrificing statistical rigour. This course equips you with the practical skills needed to build, evaluate, and interpret Bayesian models, helping you apply modern Bayesian methods confidently across a wide range of research applications.
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Upcoming Live Courses
Spatial and Spatio-Temporal Modelling Using R-INLA (SSTM02) – Extend your INLA skills to spatial and spatio-temporal Bayesian models.
 https://prstats.org/course/spatial-and-spatial-temporal-modelling-using-r-inla-sstm02/
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Spatial Phylogenetics and the Bayesian Phylogenetic Mixed Model (BPMM01) – Bayesian phylogenetic comparative methods and spatial evolutionary modelling.
 https://prstats.org/course/spatial-phylogenetics-and-the-bayesian-phylogenetic-mixed-model-pmm-bpmm01/
Path Analysis, Structural Equations and Causal Inference for Biologists (PSCB04) – Causal modelling for biological systems.
 https://prstats.org/course/path-analysis-structural-equations-and-causal-inference-for-biologists-pscb04/
Related Recorded Courses
Introduction to Bayesian Statistics – Build a solid foundation in Bayesian inference.
 https://prstats.org/recorded-courses/
Introduction to Mixed Models – Learn hierarchical and mixed-effects modelling.
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Introduction to Generalised Linear Models – Master the regression techniques underpinning modern Bayesian modelling.
 https://prstats.org/recorded-courses/
Species Distribution Modelling – Learn the principles of ecological prediction and habitat suitability modelling.
 https://prstats.org/recorded-courses/
Introduction to R – Develop the R programming skills needed for Bayesian data analysis.
 https://prstats.org/recorded-courses/
Learn more & enrol
PR Stats course page for Bayesian Modelling Using R-INLA (BMIN04)
Questions?
Email: oliver@prstats.org

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