The objective of the course is to enable students to conceptualize, perform, and critique statistical analyses from a Bayesian perspective on an advanced level. The course covers the statistical and computational foundations of Bayesian model estimation and evaluation. The first part of the course reviews the basics of probability theory, compares the Bayesian and frequentist approaches to statistical inference, discusses methods of model checking, evaluation and expansion, and introduces advanced posterior simulation methods. The second part enables students to estimate and evaluate linear and non-linear models of cross-sectional, time series and panel data. The aim of the course is to provide a thorough understanding of the theory underlying Bayesian inference, which is sought to be achieved by following a hands-on approach to modeling and computation. Students are required to work through numerous examples drawn from real applications and research using the free software packages R and Stan.