Bayesian Multilevel Modelling using *brms* for Ecologists (BMME01) — PR StatsFrame ecological questions rigorously using *Bayesian multilevel models in R* https://www.prstats.org/course/bayesian-multilevel-modelling-using-brms-for-ecologists-bmme01/
Explore model building, uncertainty quantification, spatial and temporal autocorrelation, and species distribution modelling through a *ten‑session live online course*, designed for ecologists using R. ------------------------------ Course Details & Format - *Next Session*: October 20–24 & October 27–31, 2025 (Monday–Friday) - *Duration*: 10 days × ~4 hours/day = 40 hours total - *Schedule*: Live sessions conducted in UK time (GMT+1); all sessions recorded and available for later viewing, accommodating global participants - *Format*: Interactive learning via remote online classroom; lectures and Q&A, plus applied coding exercises ------------------------------ Who It’s For & What You’ll Gain Designed for ecologists, postgraduate researchers, analysts, and early-career scientists with basic R/RStudio experience and fundamental statistics knowledge. By the end of the course, you’ll be able to: - Frame and implement *Bayesian inference*, including priors, posteriors, and credible intervals - Fit GLMs and hierarchical multilevel models using *brms* - Choose and interpret meaningful priors for ecological parameters - Diagnose and critique model fit with posterior predictive checks, WAIC, LOO-CV - Model non-Gaussian ecological data (counts, proportions, zero-inflation) - Account for *spatial and temporal autocorrelation* in hierarchical models - Build multivariate/joint species distribution models for community analysis - Visualize and clearly communicate uncertainty in model results ------------------------------ 💸 Fees & Registration - *Early bird (first 10 places)*: *£400* - *Standard fee*: *£450* Ideal participants should be comfortable installing and using R and RStudio, and have basic familiarity with functions, plotting (e.g. ggplot2), and statistics like regression. ------------------------------ Why Choose BMME01? - *Comprehensive curriculum*: From Bayesian fundamentals to advanced ecological modelling - *Focus on applied use*: Ecologically-relevant examples including spatial, temporal, count, and joint species modelling - *Expert-led sessions*: Topic-by-topic deep dives with practical exercises - *Flexible structure*: Live sessions with recordings — rewind, revisit, and learn at your pace - *Learning continuity*: Bring and work with your data, and get feedback via guided exercises and peer interaction ------------------------------ Build multilevel models with *confidence*, interpret ecological data with *nuance*, and communicate results with *clarity*. Email [email protected] with any questions -- 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
