Introduction to Bayesian Statistics with R

Author

Florian Hartig

Published

September 1, 2022

Preface

This course provides a practical introduction to Bayesian inference covering both the theory and application of Bayesian methods using a number of examples motivated from the biological and environmental sciences, including

  • Introduction to concepts of Bayesian statistics (Priors, Likelihoods, etc.)
  • Sampling methods (e.g. Markov Chain Monte Carlo) and model specification languages and frameworks (STAN, brms, BayesianTools)
  • Workflow of Bayesian inference, including model checks, model specification etc.
  • Bayesian model choice and model selection
  • Discussion of common hierarchical model structures, including mixed models, error in variable models, etc.

This material assumes prior knowledge of standard statistical methods and concepts (tests, regressions, p-value, power, CIs, …) and the ability to apply those in R. At the University of Regensburg, this knowledge would be taught in the Bachelors Biology Lecture “Statistik und Bioinformatik” (lecture notes in German here), and the block course “Introduction to statistics in R”. If you didn’t take those or comparable courses, you should at least try to get some basic understanding of R before proceeding with this book. On top of that, a good grasp of GLMMs would be helpful for following this course. As a reference, have at my lecture notes on for the course Advanced Regression Models.

I want to acknowledge that much of this material was inspired by a serious of courses and summer schools that I did in various combinations of people, but mostly with my colleagues Jörn Page, Joe Chipperfield and Björn Reineking. Those past courses inlcuded

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