312.231 (22S) Bayesian Statistics, exercises
Überblick
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- Lehrende/r
- LV-Titel englisch Bayesian Statistics, exercises
- LV-Art Übung (prüfungsimmanente LV )
- LV-Modell Präsenzlehrveranstaltung
- Semesterstunde/n 1.0
- ECTS-Anrechnungspunkte 2.0
- Anmeldungen 8 (25 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 01.03.2022
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
After successful completion, students are familiar with traditional and modern concepts of statistical inference and prediction within the Bayesian paradigm. They can independently build statistical models for various problem settings, and interpret their findings coherently.
Lehrmethodik
Lecture, exercises, case studies.
Inhalt/e
Preliminary outline (to be adjusted to students' prior knowledge):
- From Bayes’ Rule to Bayes’ Theorem
- A First Bayesian Analysis of Count Data and Proportions
- The Bayesian Approach to Regression Modeling of Normal and Non-Normal Data
- Bayesian Predictive Analysis and Model Diagnostics
- Bayesian Model Selection
- Computational Tools for Bayesian Inference
- Selected MCMC Methods for Computational Bayesian Inference
- Computational Tools for Model Comparison and Model Specification Uncertainty
- If time allows and depending on students' interests: Bayesian Time Series Analysis
- If time allows and depending on students' interests: State Space Modeling and Time-Varying Parameter Models
- If time allows and depending on students' interests: Hierarchical Bayesian Models
- If time allows and depending on students' interests: Bayesian Factor Analysis
Erwartete Vorkenntnisse
Elementary probability calculus
Curriculare Anmeldevoraussetzungen
To maximize the learning outcome, please combine with "Bayesian Statistics" (lecture).
Literatur
TBA
Prüfungsinformationen
Geänderte Prüfungsinformationen (COVID-19 Ausnahmeregelung)
If needed, we'll switch to an online setting.
Prüfungsmethode/n
Weekly exercises (to be solved and presented by the students) and case studies (to be solved and handed in).
Prüfungsinhalt/e
Contents of lecture, exercises, and case studies.
Beurteilungskriterien/-maßstäbe
Weekly exercises (to be solved and presented by the students) and case studies (to be solved and handed in).
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Doktoratsprogramm Modeling-Analysis-Optimization of discrete, continuous and stochastic systems
(SKZ: ---, Version: 16W.1)
-
Fach: Modeling-Analysis-Optimization of discrete, continuous and stochastic systems
(Pflichtfach)
-
Modeling-Analysis - Optimization of discrete, continuous and stochastic systems (
0.0h XX / 0.0 ECTS)
- 312.231 Bayesian Statistics, exercises (1.0h UE / 2.0 ECTS)
-
Modeling-Analysis - Optimization of discrete, continuous and stochastic systems (
0.0h XX / 0.0 ECTS)
-
Fach: Modeling-Analysis-Optimization of discrete, continuous and stochastic systems
(Pflichtfach)
- Masterstudium Mathematics
(SKZ: 401, Version: 18W.1)
-
Fach: Applied Statistics
(Wahlfach)
-
5.1 Bayesian Statistics (
1.0h UE / 2.0 ECTS)
- 312.231 Bayesian Statistics, exercises (1.0h UE / 2.0 ECTS)
-
5.1 Bayesian Statistics (
1.0h UE / 2.0 ECTS)
-
Fach: Applied Statistics
(Wahlfach)
- Masterstudium Mathematics
(SKZ: 401, Version: 18W.1)
-
Fach: Applied Mathematics
(Wahlfach)
-
Lehrveranstaltungen aus den Vertiefungsfächern (
0.0h XX / 12.0 ECTS)
- 312.231 Bayesian Statistics, exercises (1.0h UE / 2.0 ECTS)
-
Lehrveranstaltungen aus den Vertiefungsfächern (
0.0h XX / 12.0 ECTS)
-
Fach: Applied Mathematics
(Wahlfach)
- Doktoratsstudium Doktoratsstudium der Technischen Wissenschaften
(SKZ: 786, Version: 12W.4)
-
Fach: Studienleistungen gem. § 3 Abs. 2a des Curriculums
(Pflichtfach)
-
Studienleistungen gem. § 3 Abs. 2a des Curriculums (
16.0h XX / 32.0 ECTS)
- 312.231 Bayesian Statistics, exercises (1.0h UE / 2.0 ECTS)
-
Studienleistungen gem. § 3 Abs. 2a des Curriculums (
16.0h XX / 32.0 ECTS)
-
Fach: Studienleistungen gem. § 3 Abs. 2a des Curriculums
(Pflichtfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
-
Wintersemester 2024/25
- 312.231 UE Bayesian Statistics, exercises (1.0h / 2.0ECTS)
-
Wintersemester 2023/24
- 312.231 UE Bayesian Statistics, exercises (1.0h / 2.0ECTS)
-
Sommersemester 2020
- 312.231 UE Bayesian Statistics, exercises (1.0h / 2.0ECTS)
-
Sommersemester 2019
- 312.231 UE Bayesian Statistics, exercises (1.0h / 2.0ECTS)