Bayesian statistics Basic Information M146(2+0+2) - 5 ECTS credits The goal of the course is to introduce students to the fundamental concepts of Bayesian statistics and to compare them with frequentist statistics. It also aims to train students to apply Bayesian methods to practical problems using real data. You can access the course content at the following link: PDF Basic literature P. D. Hoff, A First Couse in Bayesian Statistical Methods, Springer, 2009. A. A. Johnson, Q. O. Miles, M. Dogucu, Bayes Rules!: An Introduction to Applied Bayesian Modeling. Chapman; Hall/CRC, 2022. W. M. Bolstad, Introduction to Bayesian statistics, John Wiley, 2007. A. Gelman, J. B. Carlin, H. S. Stern, D. B. Rubin, Bayesian Data Analysis, Second Edition, Chapman & Hall/CRC, 2004. Additional literature P. M. Lee, Bayesian Statistics: An Introduction, Fourth Edition, Wiley, 2012. J. Albert, Bayesian Computation with R, Second Edition, Springer, 2009. F. J. Samaniego, A comparision of the Bayesian and frequentist approaches to estimation, Springer, 2010. L. E. Bain and M. Engelhardt – Introduction to Probability and Mathematical statistics, Brooks/Cole, Cengage Learning, 1992. D. Gamerman, H.F. Lopes, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, 2nd Edition, Taylor and Francis, 2007. A.F.M. Smith, J. M. Bernardo, Bayesian Theory, Wiley, 1994. R. P. Dobrow, Introduction to Stochastic Processes with R. John Wiley & Sons., 2016 Teaching materials The materials are available on the internal Teams channel of the course, through which all internal communication takes place. Students are required to register on the course’s Teams channel. The channel code for joining the course can be found in the schedule.