WS 24/25

Einschreibeschlüssel: WiSo2425

People and Dates:

This bachelor seminar revisits these competing paths and critically compares them to one another. We will read introductory texts to get (more) familiar with frequentist, Bayesian and fiducial inference. We will learn about their differences with respect to both mathematical intricacies and philosophical underpinnings.

The seminar is intended as an introductory course, focusing on very basic concepts and foundational knowledge. Participants should have attended the courses on “statistical inference I and II” (“Statistik III” and “Statistik IV”), but no explicit prior knowledge on the frequentism, Bayesianism, and fiducialism debates is required. We will work with two modern textbooks [1,2], one of which [2, Part I] especially targets novices unfamiliar with the subject. We also welcome interested minor students in their final year. The seminar will be held in English.

[1] Berger, James, Meng, Xiao-Li, Reid, Nancy, and Xie, Ming-Ge. (Eds.). (2024). Handbook of Bayesian, Fiducial, and Frequentist Inference. CRC Press.

[2] Efron, Bradley, and Trevor Hastie. Computer age statistical inference, algorithms, evidence, and data science. student edition. Cambridge University Press, 2021.

Official statistical agencies are the central information service providers in a democratically organized society, informing politics, business and society about current economic, social and increasingly also ecological developments. In this way, official statistics form an important basis for informed decisions. At the same time, official statistics act as a control authority, especially for politics, by empirically reflecting the consequences of decisions and actions. To meet this responsibility, official statistics production underlies a strict methodology and high-quality standards. 

Against this background, there has also been an intensive discussion about the opportunities and challenges complex statistical modelling, machine learning techniques, and new data sources deliver. This module aims to prepare students for participation in the discourse between academic and official statistics and the numerous opportunities for cooperation with public data producers. It provides an insight into typical questions and modes of argumentation, methodological principles, and the most important products of official statistics. First, the basic principles and legal framework of official statistics, their fundamental structure in Germany/Europe and their most important supporting institutions are discussed as a basis. Further topics will then include the EU system of environmental indicators, national and international poverty measurement, the role of machine learning and big data in official statistics, selected aspects of result dissemination and statistical literacy, and access to official data sources for (own) secondary analyses, including an introduction to statistical anonymization techniques.

The course format is a mix of classical lecture style, guest lectures and inverted classroom elements with in-person discussion and deepening of the topics. The course is compulsory for the EMOS (see below) variant; all other students can choose it as an elective module.

Enrollment Key: emosa

Organisation:

Vorlesungen: Mittwoch, 10.00 - 12.00, Schellingstr. 3 (S) - S 001 (wöchentlich)

Vorlesungen: Donnerstag, 10.00 - 12.00, Schellingstr. 3 (S) - S 005 (wöchentlich)

Übung 1: Dienstag, 14.15 - 15.45, Geschw.-Scholl-Pl. 1 (D) - D 209  (wöchentlich)

Übung 2: Donnerstag, 14.15 - 15.45, Geschw.-Scholl-Pl. 1 (D) - D 209 (wöchentlich)

Student enrollment key: InfStat12025!

This seminar is designed to be taken together with the course "Causal Inference" as an opportunity to explore topics from the lectures in more depth. Taking the course "Causal Inference" is not required, but background knowledge on causality is strongly recommended.

Because of its link to the course, the topics of the seminar are strongly related: we will read and discuss recent research papers on the identification of causal effects with a focus on applications in economics.

Welcome to the course "Statistics for Geosciences" in winter term 24/25!

First meeting: Wednesday, October 16, 15h00 (UTC/GMT +2) via zoom

Enrolment key: rose-diagram

Enrollment key: css2024

The seminar will present and discuss current statistical analyses of the climate crisis. It addresses central topics from a joint research project (CLIMEX II) with the LMU Chair of Physical Geography and Environmental Modelling. There will be two focal points.

1. Statistical modelling of climate change, extreme climatic events and natural hazards. This involves uncertainty quantification of climate models, drought forecasts for Bavaria, modelling of extreme events

2. Effects of climate change on human health. In particular, models that attempt to analyse the current and future adverse effects (e.g. number of additional deaths) caused by climate change will be discussed. In particular, analyses by the Intergovernmental Panel on Climate Change (IPCC) will be addressed.

Students are expected to engage intensively with the scientific literature, present the statistical methodology for the chosen topic and/or replicate results from the literature. Students will work on their seminar topic in groups of 2 Persons, one statistics and one geography student. The Seminar meetings, presentations and final report will be in English. We also recommend good skills in git and GitHub for the statistics students. It is possible to present your topics and, if suitable, to work on them in the seminar. The final results of the Seminar will be published as Chapters online in a Book: https://henrifnk.github.io/Seminar_ClimateNStatistics/

This tutorial is aimed at Master's students seeking to refresh or broaden their basic mathematics skills.

Enrolment key (Einschreibeschlüssel): MathIsFun

Das WP "Statistik und Kirche" bietet eine umfassende Einführung in die statistische Analyse von Kirchenmitgliedschaften. Im WP werden Datenquellen der katholischen Kirche betrachtet, um ein tiefgehendes Verständnis für die Erhebung und Analyse von Mitgliedschaftsdaten zu entwickeln. Themen wie Datenqualität, Kirchenmitgliedschaftsuntersuchungen (KMU), deskriptive Statistik und Datenvisualisierung stehen im Mittelpunkt.

Das WP richtet sich sowohl an Theologie- als auch Statistikstudierenden, die in interdisziplinären Teams zusammenarbeiten werden. Diese Zusammenarbeit ermöglicht einen Austausch verschiedener Perspektiven und fördert das gemeinsame Erarbeiten von Erkenntnissen. Eine Mitgliedschaft in der katholischen Kirche ist selbstverständlich KEINE Voraussetzung für die Teilnahme am WP als Statistikstudierender.

BA Statistik und Data Science: WP 8 Einblicke in ausgewählte Anwendungsfelder von Statistik und Data Science

BA Statistik und Data Science: WP 11 Spezielle Themen der Statistik und Data Science

Seminar (für die kath. Theologie/fällt nicht unter die Seminare in der Statistik!) Pastoraltheologie / Kirchenrecht

Einschreibeschlüssel: 

Schedule:

• The enrollment key is "stat_inf_w2425"

• The enrolment key is: "PCS2425"

In this course, we will learn how to conduct empirical analyses through the lenses of permutation- and bootstrap-based techniques. In particular, we will uncover how these nonparametric methods allow us to make reliable inferences under minimal assumptions in a data-driven way.

Ausgehend von der linearen Modellierung werden komplexe und flexible Regressionsmodelle (generalisierte lineare Modelle und generalisierte additive Modelle) behandelt. Weiter wird ein Überblick über wichtige multivariate Analysetechniken, wie Klassifikation, Diskriminanzanalyse und Clusteranalyse und Modelle für latente Variablen, gegeben.

Termine und Personen:

Enrollment Key: w2425_seminar_fma

Teacher: Walter J. Radermacher
Runtime: 1. October 2024 - 10. October 2023
Format: Self-learning through online videos in the first week and in-person workshops for practical appliance of use cases in the second week.
Examination: Oral Exam (3 ECTS credits)
Language: English


Who is this course for? MsC Statistics and Data Science (2021, WP 28+29+40+46), BsC Statistics and Data Science (2021, WP 8+11), Statistik und Data Science als Nebenfach für Bachelor 30 ECTS (2021, WP 4+5), Statistik und Data Science als Nebenfach für Bachelor 60 ECTS (2021, WP 11+12), Statistik und Data Science als Nebenfach 30 ECTS Mathematik (2021, WP 5+6), Statistik Nebenfach 60 ECTS Bachelor Soziologie (2021, WP 10+11), WISO Mater (2010, "Ausgewählte Gebiete..." (3ECTS))

Selbsteinschreibungsschlüssel: grlgprkt

Die Veranstaltung wendet sich an Studierende im Bachelor Statistik & DataScience  (3. Semester). Das "Grundlegende Praxisprojekt" (BA Statistik und Data Science - PO 2021) ist eine Pflichtveranstaltung (Modul P 11.1).

Die Veranstaltung wird sowohl während der Vorlesungszeit (in zwei getrennten Termingruppen) als auch in den Semesterferien angeboten. Diese Moodle-Seite ist gemeinsam für alle Veranstaltungen. 
Für alle drei Blöcke finden Einführungsveranstaltungen mit Anwesenheitspflicht am 14. und 21.10.2024, 16-18 Uhr statt. Aus organisatorischen Gründen ist eine frühzeitige, separate Anmeldung für die Teilnahme während der Vorlesungszeit nötig -- schreiben Sie sich bitte in den Kurs ein und melden Sie sich dann auf der Kursseite für einen der 128 während dem Semester verfügbaren Praktikumsplätze an.

Einschreibeschlüssel: dskrpt

Master and Bachelor Seminare

Enrolment Key: Hierarchy

Person: Dr. Cornelia Oberhauser

SAS course as a 5-day block course during the semester break

Dates:

Enrolment key

• The enrolment key is: ‘saskurs2024’
  

Guest key

• The guest key is: ‘saskurs2024’
  

Master course 

• Master Statistics & Data Science – WP44 Advanced Statistical Modelling
• ESG Data Science – Elective
• Master Statistik (mit WiSo), Biostatistik – Ausgewählte Gebiete... oder Schätzen und Testen II (mit Zusatzleistung)
  

Bayesian methods have seen increasing use within statistics in the last couple of years. In Bayesian frameworks, prior knowledge can be integrated as prior distribution. These also allows to regularise parameters in large p>>n models via priors, up to the point where the prior becomes part of the statistical model itself.

In this course, advanced Bayesian models will be discussed. These include several types of latent models, in particular latent Gaussian fields, applied for example to time series and spatial data, mixed effect models and other structured regression models. The course will also cover different type of algorithms to assess the posterior, based on Monte Carlo methods and on approximation. Further topics include Bayesian model and variable selection and Bayesian classification.

Requirements: 

Students should be familiar with basics of Bayesian statistics.

Recommended: 

• Statistical inference
• Statistical modelling

Learning outcomes:

After successfully completing the course, students will be able 

• to understand Bayesian models 
• to apply Bayesian modelling for appropriate data
• to interpret the results of Bayesian models 
• to implement Bayesian models in standard software

Die Veranstaltung "Programmieren mit Statistischer Software (R)" wendet sich an Studenten im Bachelor Statistik und Data Science (3. Semester). Sie baut auf die Veranstaltung "Einführung in die Statistische Software" (1. Semester) auf.

Einfuehrungsveranstaltungen

Zeit: Montag 2024-10-14, 10--12 c.t., Mittwoch 2024-10-16, 16--18 c.t.
Ort: Schellingstr. 3 (S), Raum S 003

Tutorials

Termine: Montags und Mittwochs, 2024-10-21 -- 2024-12-18; 2025-01-13 -- 2025-02-05.

Zeiten sind "s.t.".

Simply called "Data Privacy" on lsf if you want to register.

Einschreibeschlüssel: dataprivacy

Schedule

• Lecture: 
  • Wednesday, 10-12 c.t.
  • Location: Oettingenstr. 67, L 155 (Raumfinder)
• Tutorial: 
  • Friday, 10 - 12 c.t.
  • Location: Oettingenstr. 67, B U101 (Raumfinder)

Enrollment key

• Enrollment key: hallucination