Bayesian Modelling Using R-INLA (BMIN04) – Applications for Evolutionary Biology

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, with methods that are increasingly relevant across evolutionary biology, ecology, and population genetics.
Bayesian modelling has become an essential tool for answering complex questions in evolutionary biology. While this course focuses on Bayesian modelling using R-INLA, the methods are highly transferable to studies of adaptation, population dynamics, comparative biology, phylogeography, quantitative genetics, and evolutionary ecology. R-INLA provides an efficient alternative to traditional MCMC methods, making sophisticated Bayesian analyses practical for large and complex biological 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?
- Evolutionary biologists and evolutionary ecologists
- Population geneticists and quantitative biologists
- Researchers working with comparative, ecological, or genomic datasets
- PhD students and quantitative researchers
- Anyone interested in applying modern Bayesian methods to evolutionary research
Why take this course?
Evolutionary datasets are often hierarchical, spatially structured, and characterised by multiple sources of uncertainty. Bayesian methods provide a powerful framework for modelling these complexities while allowing researchers to incorporate prior knowledge and quantify uncertainty throughout the analytical process.
The techniques taught in this course are applicable to a wide range of evolutionary research questions, including population dynamics, trait evolution, phylogeography, evolutionary ecology, comparative analyses, and quantitative genetics. By learning R-INLA, you'll gain the skills to fit sophisticated Bayesian models efficiently and apply modern Bayesian inference to challenging evolutionary datasets. 
Learn more & enrol
PR Stats course page for Bayesian Modelling Using R-INLA (BMIN04)
Questions?
Email: oliver@prstats.org

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