This teaching material on Bayesian Statistics originated mostly from a series of introductory course on Bayesian statistics that I have held over the last years together with various subsets of Jörn Pagel, Joseph Chipperfield and Björn Reineking, and Felix May. I will announce any upcoming courses at my blog or on twitter.
A collection of commented code examples on concepts and applications is here here. These example span the range of topics that we typically cover, including
- Concepts of Bayesian statistics (Priors, Likelihoods, etc.). Code examples here
- Sampling algorithms (MCMC, SMC). Code examples here
- The Bugs model language and its implementations in JAGS, STAN and OpenBugs. Code examples here
- Standard statistical models with Bayes. Code examples here
- (Generalized) linear models (G)LM
- (Generalized) linear mixed models (G)LMM
- Advanced model types. Code examples here
- Spatial models
- State-space models
- Bayesian model analysis and checks - code
- Model selection and averaging - code
- Approximate Bayesian Computation (ABC) - code
- Process-based models and Bayes - code
I'm working on providing proper lecture notes for this course. For the moment, I'm recommending as an introduction the free lecture notes Bayesian Basics by Michael Clark. For further reading see the textbooks and articles suggested here.
- Sept 2019 Münster, Germany
- Feb 2019 Bangkok, Thailand
- Sept 2018 Bergen Norway
- April 2018 Frankfurt, Germany
- Sept 2017 Bergen Norway
- Sept 2015 Bergen Norway
- Leipzig 2015
- Bergen 2014
- Freiburg 2013
- Göttingen 2013
- Bayreuth 2012
- Bayreuth 2011