This is an introductory course in Bayesian statistical modelling. We will read chapters of the textbook by McElreath (2020, 2nd edition), discuss the content and apply the methods in exercises using the brms package in R.
Learning objectives
Students ...
… have experienced and understood the fundamental philosophy behind Bayesian probability theory,
… have acquired the skills to do Bayesian analysis using the brms package in R,
… know which resources to consult for further study.
Topics
Format
The mode of working is a mix of independent textbook study; collective discussion; independent and collective problem solving; homework; and lecture-style inputs from the teacher as needed.
The open-source software STAN will be used via the brms package R. An introduction to and help with brms/STAN will be provided. Students need a good working knowledge of R!
Homework will be submitted using R Markdown.
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.
McElreath. 2020 (2nd edition). Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press
Towards the end of the semester the students select an individual exam project involving data analysis using brms, which has to be submitted via R Markdown, just like an extended 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 2 mal im Vorlesungsverzeichnis SoSe 2024 gefunden: