Description
This 2-day short course is designed to provide participants with a comprehensive understanding of advanced Bayesian structured additive regression models and their practical implementation using the R programming language. The course begins with foundational sessions covering Bayesian regression models, structured additive models, and spatial/temporal modeling. Participants will gain hands-on experience using popular R packages such as bamlss, brms, mgcv and stan. The second day delves into advanced topics, including interactions, nonlinear effects, model selection, and the integration of Bayesian methods with machine learning. The course emphasizes practical applications through case studies and exercises, allowing participants to apply learned concepts to real-world problems. The addition of Bayesian machine learning and big data handling provides a holistic view of contemporary Bayesian statistical modeling. By the end of the course, participants will be equipped with the knowledge and skills to tackle first complex data analysis challenges using Bayesian structured additive (distributional) regression models in R.| Period | 19-Sept-2024 → 20-Sept-2024 |
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