Online training - Computational Bayesian methods using brms in R Where and When: Online (Zoom) - 14-18 February 2022 This course provides a relatively accessible and technically non-demanding introduction to the basic workflow for fitting different kinds of linear models using a powerful front-end R package for Stan called brms. We assume familiarity with R. Participants will benefit most if they have previously fit linear models and linear mixed models (using lme4) in R, in any scientific domain. No knowledge of calculus or linear algebra is assumed, but basic school level mathematics knowledge is assumed (this will be quickly revisited in class). have become familiar with the foundations of Bayesian inference be able to fit a range of multiple regression models and hierarchical models for normally distributed data, for log-normal, and binomially distributed data. be able to communicate the results of a Bayesian analysis know how to select priors for their models using prior predictive checks know how to assess the descriptive accuracy of a model using posterior predictive checks.Course website: https://www.physalia-courses.org/courses-workshops/course46/ Here you can find the full list of our courses and Workshops: [ https://www.physalia-courses.org/courses-workshops/ ]( https://www.physalia-courses.org/courses-workshops/ ) Should you have any questions, please feel free to contact us: i...@physalia-courses.org -------------------- Carlo Pecoraro, Ph.D Physalia-courses DIRECTOR i...@physalia-courses.org mobile: +49 17645230846 Follow us on [ Twitter ]( https://twitter.com/Physacourses ) [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
