Winter 2022/23: Bayesian Statistics I
Contents
Bayesian thinking differs from frequentist statistics in its interpretation of probability and uncertainty. It complements the existing statistical toolbox with powerful methods for simulation and inference. The lecture Bayesian Statistics I aims to familiarize the students to the Bayesian approach. The course deals with the theoretical fundamentals and the principles of estimating, testing, forecasting and model assessment. In addition, Bayesian regression concepts and computer- intensive simulation methods such as Markov chain Monte Carlo (MCMC) are introduced.
General information
Lecturer: Houda Yaqine
Type: Lecture
Recommended prerequisites: Good knowledge of statistics (esp. (conditional) densities/probabilities, likelihood inference, regression) and R
Module allocation: see eKVV
Dates: The lecture classes take place in person at the university on Fridays as listed in the following. Please check this page regularly for updates!
|
Date |
Type |
Time |
Format |
|
21.10.2022 (Fri) |
lecture |
16:15-17:52 |
in person (X-E0-002) |
|
28.10.2022 (Fri) |
lecture |
16:15-17:52 |
Material provided via LernraumPlus |
|
04.11.2022 (Fri) |
lecture |
16:15-17:52 |
in person (X-E0-002) |
|
11.11.2022 (Fri) |
lecture |
16:15-17:52 |
in person (X-E0-002) |
|
18.11.2022 (Fri) |
lecture |
16:15-17:52 |
in person (X-E0-002) |
|
25.11.2022 (Fri) |
lecture |
16:15-17:52 |
in person (X-E0-002) |
|
02.12.2022 (Fri) |
lecture |
16:15-17:52 |
in person (X-E0-002) |
|
09.12.2022 (Fri) |
lecture |
16:15-17:52 |
in person (X-E0-002) |
|
16.12.2022 (Fri) |
lecture |
16:15-17:52 |
Recorded (online) |
|
23.12.2022 (Fri) |
lecture |
16:15-17:52 |
in person (X-E0-002) |
|
13.01.2023 (Fri) |
lecture |
16:15-17:52 |
in person (X-E0-002) |
|
20.01.2023 (Fri) |
lecture |
16:15-17:52 |
in person (X-E0-002) |
|
27.01.2023 (Fri) |
lecture |
16:15-17:52 |
in person (X-E0-002) |
|
03.02.2023 (Fri) |
lecture |
16:15-17:52 |
in person (X-E0-002) |
Literature
- Lee: Bayesian Statistics. Wiley, 4th edition.
- Gelman et al.: Bayesian Data Analysis. CRC Press, 3rd edition.Held & Sabanés Bové: Applied Statistical Inference. Springer.
- B. Lambert: A Student’s guide to Bayesian Statistics. Sage, 2018.
- R. McElreath: Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC, 2020.