Bayesian Statistics and Data Analysis

The course is an introduction to Bayesian Statistics and Dana Analysis. The course covers core ideas in Bayesian inference, such as Bayesian epistemology, Bayes theorem, prior and posterior distributions, analytical derivation of posterior distributions and conjugacy. In addition, modern methods for posterior simulation are introduced in the form of Markov Chain Monte Carlo, Hamiltonian Monte Carlo and the probabilistic programming language Stan. The Bayesian inference methods and principles on model assessment and model selection are studied theoretically and practically through continuous computer assignments and a smaller data analysis project.