Bayesian Multilevel Modelling Using *brms* for Ecologists (BMME01) www.prstats.org/course/bayesian-multilevel-modelling-using-brms-for-ecologists-bmme01 <https://www.prstats.org/course/bayesian-multilevel-modelling-using-brms-for-ecologists-bmme01?utm_source=chatgpt.com>
*Dates:* October 20–24 and 27–31, 2025 *Format:* Live online course (4 hours per day) *Fee:* £450 (standard) | £400 (early bird for the first 5 registrations) SOLD OUT Bayesian methods are rapidly becoming the standard for ecological data analysis. They offer a flexible, transparent, and robust approach to dealing with uncertainty, hierarchical data structures, and complex ecological processes. If you work with ecological data and want to extend your modelling skills beyond classical statistics, this course will give you the tools and understanding to do so with confidence. *Bayesian Multilevel Modelling using brms for Ecologists (BMME01)* is an intensive 10-day live online course designed to provide ecologists, environmental scientists, and applied researchers with a solid grounding in Bayesian hierarchical modelling using the *brms* package in R. Over the course of ten sessions, participants will be guided step by step through the key concepts and practical applications of Bayesian inference, with a strong focus on real ecological examples. The course combines clear, structured teaching with extensive hands-on coding and interpretation. Course Overview Participants will begin by reviewing the fundamentals of Bayesian inference—priors, posteriors, and credible intervals—before moving on to the construction and interpretation of generalised linear and multilevel models. You will learn how to fit and diagnose models for a range of ecological data types, including continuous, count, binary, and zero-inflated data, as well as how to address common challenges such as spatial and temporal autocorrelation. Throughout, you will work with real-world datasets and gain practical experience in coding models using *brms*, an R package that provides a user-friendly interface to *Stan*, one of the most powerful Bayesian computation frameworks available. By the end of the course, you will have the skills to build, diagnose, and interpret complex Bayesian models relevant to your own ecological research. Key Topics Include - The foundations of Bayesian inference and model formulation - Understanding priors, posteriors, and credible intervals - Generalised linear models (GLMs) and extensions to hierarchical models - Random effects, nested structures, and partial pooling - Modelling count, binary, zero-inflated, and multivariate ecological data - Incorporating spatial and temporal structures - Model checking, convergence diagnostics, and posterior predictive checks - Interpreting and communicating uncertainty in ecological research Who Should Attend This course is aimed at ecologists, environmental scientists, conservation biologists, statisticians, and postgraduate researchers who already have a working knowledge of R (data import, manipulation, and basic plotting) and want to develop practical expertise in Bayesian multilevel modelling. No prior experience with Bayesian methods is required—concepts and techniques are introduced progressively, with plenty of guided practice and individual feedback. Why Choose This Course - Expert instruction from *Dr Niamh Mimnagh*, an experienced statistical ecologist and educator - A carefully balanced mix of lectures, demonstrations, and hands-on coding - Small-group format encouraging discussion and individual support - All course materials, R scripts, and datasets provided - Access to recorded sessions for 30 days after the course - Certificate of attendance upon completion Registration Places are limited to ensure an interactive learning experience. The early bird rate of £400 applies to the first five registrants. Standard registration is £450. For full details, schedule, and registration, visit: www.prstats.org/course/bayesian-multilevel-modelling-using-brms-for-ecologists-bmme01 <https://www.prstats.org/course/bayesian-multilevel-modelling-using-brms-for-ecologists-bmme01?utm_source=chatgpt.com> -- Oliver Hooker PhD. PR stats [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
