312.231 (23W) Bayesian Statistics, exercises

Wintersemester 2023/24

Anmeldefrist abgelaufen.

Erster Termin der LV
05.10.2023 15:00 - 16:00 N.2.01 On Campus
... keine weiteren Termine bekannt

Überblick

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 22 (25 max.)
Organisationseinheit
Unterrichtssprache Englisch
LV-Beginn 05.10.2023
eLearning zum Moodle-Kurs

Zeit und Ort

Liste der Termine wird geladen...

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

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

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 Benotungsschema

Position 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)
  • 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)
  • 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)
  • Masterstudium Mathematics (SKZ: 401, Version: 22W.1)
    • Fach: Statistics and Probability (Pflichtfach)
      • 3.1 Bayesian Statistics ( 1.0h UE / 2.0 ECTS)
        • 312.231 Bayesian Statistics, exercises (1.0h UE / 2.0 ECTS)
          Absolvierung im 1. Semester empfohlen
  • 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)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Sommersemester 2022
  • 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)