This is a course in Bayesian statistical modelling based on the textbook by McElreath (2020, 2nd ed.).
… have experienced and understood the fundamental philosophy behind Bayesian probability theory,
… have acquired the skills to do Bayesian analysis in STAN via interfaces from R,
… know which resources to consult for further study.
The mode of working is a mix of textbook study and collective discussion; exercises and collective problem solving; homework; and some lecture-style inputs from the teacher as needed. The students are required to take an active role in shaping the direction of the course.
The open source software STAN will be used from R via the 'brms' package. An introduction to and help with STAN and brms will be provided. A firm background in classical statistics and the software R is required, equivalent to a full grasp of “Quantitative Methods for Geographers”.
Allocation of places
Due to the mode of working in this course places are limited. Students are required to register via Agnes. Priority will be given to 4th semester students of the Global Change Geography Master. Remaining places will be allocated in the 1st class.
McElreath 2020 (2nd ed.). Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press
Gelman et al. 2020. Regression and Other Stories. Cambridge University Press
Students can choose from a set of projects at the end of the semester which are like an extended homework. These need to be handed in using R Markdown just like the homework.
A firm background in classical statistics and the software R is required, equivalent to a full grasp of “Quantitative Methods for Geographers”.
Die Veranstaltung wurde 1 mal im Vorlesungsverzeichnis SoSe 2021 gefunden: