tgstoecker / LearningBayes

An introduction to Bayesian statistics

Home Page:http://florianhartig.github.io/LearningBayes

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

Synopsis

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.

Topics, lecture notes and code

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.

Past courses:

About

An introduction to Bayesian statistics

http://florianhartig.github.io/LearningBayes


Languages

Language:R 96.0%Language:TeX 4.0%