Schedule: 12.09.2016 - 09.12.2016
Teaching Period (valid 01.08.2018-31.07.2020):
Learning Outcomes (valid 01.08.2018-31.07.2020):
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.
Content (valid 01.08.2018-31.07.2020):
Bayesian probability theory and bayesian inference. Bayesian models and their analysis. Computational methods, Markov-Chain Monte Carlo.
Workload (valid 01.08.2018-31.07.2020):
Lectures 10x2h, computer exercises 10x2h, independent studying (text book, programming, home exercise report), final exam
Substitutes for Courses (valid 01.08.2018-31.07.2020):
Replaces courses BECS-E2601 Bayesian Data Analysis, Becs-114.2601 Bayesian Modelling and Becs-114.1311 Introduction to Bayesian Statistics.
Prerequisites (valid 01.08.2018-31.07.2020):
Differential and integral calculus, basics of probability and statistics, basics of programming (R or Python). Recommended: matrix algebra.
Grading Scale (valid 01.08.2018-31.07.2020):
0 - 5