Bayesian Modelling with R-INLA *Five-Day Intensive Online Course* Marine-mammal research increasingly relies on advanced statistical tools to analyse complex ecological data: spatial surveys, detection-non-detection datasets, telemetry tracks, abundance estimates, behavioural data, and multi-level hierarchical structures. *Bayesian Modelling using R-INLA (BMIN03)* provides a comprehensive, practical introduction to modern Bayesian methods that are directly applicable to these challenges. This five-day live-online course is designed for ecologists and conservation scientists who want to apply *efficient Bayesian modelling* to their data, using the R-INLA framework. Why Marine-Mammal Scientists Benefit from R-INLA - *Spatial and spatio-temporal modelling* are central to marine-mammal ecology; INLA excels at these models due to its efficient latent Gaussian approach. - *Large datasets* from aerial surveys, eDNA transects, passive acoustic monitoring, or satellite telemetry can be modelled more efficiently than with traditional MCMC. - INLA supports *complex hierarchical structures*, allowing you to combine environmental covariates, survey design effects, behavioural information, and individual variation within a unified Bayesian framework. What You Will Learn - The core ideas behind *Bayesian inference* and how INLA provides fast, accurate approximations ideal for large ecological datasets. - How to build and fit *generalised linear, mixed-effects, spatial, and spatio-temporal models* commonly used in marine-mammal population analysis. - How to incorporate *latent effects*, custom priors, random fields, and structured dependencies — useful for modelling habitat use, movement, distribution, and abundance. - How to analyse *line-transect and distance-sampling-style data*, telemetry data, occupancy models, and hierarchical datasets with individual- or site-level effects. - How to interpret posterior outputs, generate predictions, map spatial distributions, assess uncertainty, and communicate Bayesian results effectively. Course Format - *Five days*, 7 hours per day, combining theory, practical coding exercises, and guided model-building. - *Live online* sessions with full recordings provided. - Full access to teaching materials plus *30 days of post-course support*. Who Should Attend - Marine-mammal ecologists, conservation biologists, environmental statisticians and analysts who work in R. - Researchers involved in *abundance estimation, density surface modelling, telemetry studies, habitat modelling, impact assessment, or long-term monitoring*. - Scientists familiar with basic statistical modelling (e.g., GLMs, mixed models) but who want to move into Bayesian approaches. *Next course date:* 23–27 February 2026 (live online) *Course fee:* £500 Advance your ability to model complex ecological systems and produce defensible, transparent, and uncertainty-aware analyses. Learn more or register: https://prstats.org/course/bayesian-modelling-using-r-inla-bmin03/ <https://prstats.org/course/bayesian-modelling-using-r-inla-bmin03/?utm_source=chatgpt.com> -- Oliver Hooker PhD. PR stats