Contents: Bayesian probability theory and Bayesian inference. Bayesian models and their analysis. Computational methods, Markov-Chain Monte Carlo.
After the course, the student can explain the central concepts in Bayesian statistics, and name steps of the Bayesian modeling process. The student can recognize usages for common (i.e. those presented during the course) statistical models, and formulate the models in these situations. The student can compare the most popular Bayesian simulation methods, and implement them. The student can use analytic and simulation based methods for learning the parameters of a given model. The student can estimate the fit of a model to data and compare models.
Assessment: Examination and exercises.
Prerequisites: Basics in probability calculus. R, Python (or Matlab).