Bayesian Statistics and MCMC Methods give you a toolkit for modern empirical research under uncertainty. You will learn how priors, likelihoods, and Bayes’ theorem combine into posterior inference, then turn theory into practice through computational methods used in real data problems. The course moves from the basic foundations of Bayesian statistics to modern simulation tecniques, including Gibbs sampling and Metropolis-Hastings. We will apply the methods to econometric models such as multiple linear regression and Tobit models. This course is rigorous, hands-on, and directly relevant for research in economics, finance and data science.