Bayesian Modelling Using R-INLA (BMIN04) – Applications for Evolutionary Biology https://prstats.org/course/bayesian-modelling-using-r-inla-bmin04/ Only 3 places left! Delivered by Dr. Virgilio Gómez-Rubio, a statistician with extensive expertise in Bayesian inference, spatial statistics, statistical computing, and the application of Integrated Nested Laplace Approximation (INLA) to complex data. He is the author of Bayesian Inference with INLA and has extensive experience developing and applying Bayesian and spatial modelling methods in R. Learn how to use Bayesian modelling and R-INLA to analyse hierarchical, spatial, temporal, and other complex datasets, with methods highly applicable to evolutionary biology, evolutionary ecology, and comparative research. Evolutionary datasets frequently contain hierarchical structure, phylogenetic or population-level grouping, spatial and temporal dependence, repeated observations, missing data, and multiple sources of uncertainty. Bayesian modelling provides a powerful framework for handling these complexities, while INLA provides an efficient alternative to conventional MCMC for fitting a broad class of sophisticated Bayesian models. What you'll gain A strong understanding of Bayesian inference and prior specification Practical experience fitting Bayesian models using R-INLA Skills fitting GLMMs and multilevel models Experience modelling hierarchical biological datasets Practical training in spatial Bayesian modelling Skills for analysing time-series and spatiotemporal data Understanding of latent effects and advanced INLA model structures Experience comparing models using DIC and WAIC Skills interpreting posterior distributions and quantifying uncertainty Approaches for handling missing values and imputation Confidence applying Bayesian modelling workflows to complex evolutionary datasets Course format 5-day live, instructor-led online course Combination of lectures and hands-on practical exercises Dedicated practical sessions throughout Opportunity to discuss participants' own datasets and research questions All code, datasets, and presentation materials provided All sessions recorded 30 days of recording access and post-course email support Who is this course for? Evolutionary biologists Evolutionary ecologists Population and quantitative biologists Evolutionary geneticists Researchers working with spatial or temporal evolutionary data Researchers analysing hierarchical and repeated-measures datasets Researchers studying variation among individuals, populations, or species PhD students and researchers wanting to develop Bayesian modelling skills in R Why take this course? Evolutionary datasets often have structures that are difficult to accommodate with simple statistical models. Researchers may need to account simultaneously for variation among individuals and populations, repeated measurements, geographic structure, temporal dependence, environmental effects, and uncertainty. Bayesian hierarchical modelling provides a flexible framework for these problems, and R-INLA makes many complex Bayesian models computationally efficient to fit. This makes the approach particularly useful for large or structured biological datasets where conventional MCMC methods can become computationally demanding. The methods taught have potential applications to evolutionary ecology, population dynamics, geographic variation, spatial evolutionary processes, longitudinal studies, comparative biological datasets, and analyses incorporating multiple levels of biological variation. Whether you're investigating variation among populations, evolutionary responses across environments, spatial or temporal patterns in traits, population dynamics, or other complex evolutionary questions, this course provides a practical framework for incorporating Bayesian inference into your research. Course dates 21–25 September 2026 14:00–21:30 UK time 5 days | Live online | £500 Learn more & enrol https://prstats.org/course/bayesian-modelling-using-r-inla-bmin04/ Questions? Email: oliver@prstats.org (to subscribe/unsubscribe the EvolDir send mail to evoldir@evoldir.net)