Bayesian Modelling Using R-INLA (BMIN04) – Applications for Marine Mammal Research 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 highly applicable to marine mammal ecology, conservation, and population assessment. Marine mammal datasets are often hierarchical, spatially structured, and collected over long time periods from multiple survey platforms. While this course teaches Bayesian modelling using the powerful R-INLA framework, the methods are directly transferable to marine mammal research, including abundance estimation, habitat modelling, telemetry, passive acoustic monitoring, occupancy studies, and population dynamics. R-INLA provides a computationally efficient alternative to traditional MCMC methods, making sophisticated Bayesian analyses practical for large ecological 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? * Marine mammal researchers looking to incorporate Bayesian methods into their analyses * Marine ecologists and conservation scientists * Researchers analysing survey, telemetry, acoustic, or environmental datasets * PhD students and quantitative environmental scientists * Anyone interested in applying modern Bayesian statistics to marine ecological data Why take this course? Marine mammal research frequently involves complex datasets that contain spatial structure, repeated observations, imperfect detection, and multiple sources of uncertainty. Bayesian methods provide an ideal framework for analysing these data while producing intuitive estimates of uncertainty. The techniques taught in this course are applicable to a wide range of marine mammal research questions, including habitat suitability modelling, abundance estimation, occupancy analysis, movement ecology, passive acoustic monitoring, spatial risk assessment, and conservation planning. By learning R-INLA, you'll gain the skills to fit sophisticated Bayesian models quickly and efficiently, allowing you to answer complex ecological questions that are difficult to address using traditional statistical approaches. Learn more & enrol PR Stats course page for Bayesian Modelling Using R-INLA (BMIN04) https://prstats.org/course/bayesian-modelling-using-r-inla-bmin04/ Questions? Email: oliver@prstats.org Oliver Hooker Managing Partner [signature_68833404] Advanced Training for Researchers in the Life Sciences coursesinfo@prstats.org <http://oliver@prstats.org/> | www.prstats.org<http://www.prstats.org/>