SS23

Entropy is defined as a measurable physical property that is most commonly associated with a state of disorder, randomness, or uncertainty. It is strongly connected with probability distributions and the principle of maximum entropy can be very useful in statistical inference, in particular in Bayes statistics. In this course we will introduce the concept of entropy in the context of information theory as well as apply the concepts on real data sets.

Syllabus

1. Introduction and Preview
2. Entropy, Relative Entropy, and Mutual Information
3. Asymptotic Equipartition Property
4. Entropy Rates of a Stochastic Process
5. Differential Entropy
6. Information Theory and Statistics

Schedule:

Information Theory and Entropy for Master Statistics and Data Science, Master Data Science, Master Statistik, Master Biostatistik, Master Statistik WiSo.

Enrolment Key: Shannon23

Einschreibeschlüssel: Ringvorlesung

enrolment key: why?

Key: reg-cor-dat
Course kick-off  20.04. with an introductory in-person lecture.

Format:
Inverted classroom: You are expected to come prepared for both the Q&As and exercise classes -- you've watched the lecture videos, you've reviewed the slides/exercise sheets, you've asked ChatGPT to explain parts you found confusing, you've done the self-assessment quizzes, and you've posted questions you would like to see addressed during the session in the Moodle Forum. 

Schedule:

Syllabus:

1. Overview
2. Fundamentals and Properties of Stochastic Processes
3. Univariate ARIMA-Processes
4. Estimation and Forecasting of ARIMA-Models
5. Univariate GARCH-Models + Extensions
6. Selected aspects: Long Memory und Fractional Differencing, Threshold-Models

Intended audience: Advanced bachelor and master students of statistics, mathematics, computer science (Informatik), economics and business administration.

Prerequisites: Solid mathematical foundations (analysis and linear algebra), basic knowledge in econometrics (econometrics 1) or statistics (linear models).

Record of Achievement:

Dieser Kurs ist eine Wiederholung ausschließlich für die alte Prüfungsordnung 2010 für den Bachelor Statistik. Insofern der Kurs Wahrscheinlichkeitstheorie und Inferenz I noch nicht bestanden worden ist, empfehlen wir dringend auf die neue Veranstaltung zur Inferenz auszuweichen.

Hierfür wird direkt zu Beginn des Semesters Material in Form eines Skriptes, Vorlesungsfolien und Übungsblätter mit deren Lösungsansätzen online gestellt und in einer monatlichen Fragestunde besteht die Möglichkeit auf einzelne Dinge einzugehen und Fragen zu stellen. 

Außerdem wird es in der Woche vor der Klausur ausführlicher Fragen zu stellen und Dinge zu wiederholen. 

Diesen Kurs zu belegen, bedeutet also ein hohes Maß an Eigenverantwortung.

Ich empfehle dringendst zu der Einführungsstunde am Donnerstag, den 20.04. um 10 - 12 Uhr zu kommen, nachdem da das Format von dem Kurs erklärt wird.

Der Einschreibeschlüssel ist: Wiederholungskurs

• This lecture covers the basics of Bayesian statistics and its practical applications
  
• The lecture is held in English. It will be held online too. 
  
• The course may be taken by students with a minor in "Statistik" or "Statistik und Data Science", as well as a major in "Statistik" (Prüfungsordnung 2010). 

Schedule:

Introduction to Bayesian Statistics for Bachelor and Minor Statistics and Data Science

Registration key: Bayes23

The enrolment key for the course is: FortStatSoftNF.

If you have questions please contact statprog@stat.uni-muenchen.de.

The course will be taught in English, more information can be found in the course itself.

Enrolment key: StatModSs23

Decision theory deals with rational decisions under uncertainty. It has high interdisciplinary importance, for example, in the analysis and support of decisions in business administration or finance (e.g. investment strategies), economics or sociology (rational choice theory), medicine (e.g. expert systems) or engineering (e.g. autonomous control). Moreover, statistical decision theory can be seen as a formal framework for choosing analysis methods (optimal tests or estimators, best classification algorithms, etc.). This general view, understanding statistics and machine learning as special cases of decision theory, plays a fundamental role in the critical analysis and problem-adequate generalization of any data-based learning procedure.

The course first discusses the general structure of decision problems, including fundamental decision principles. Then it analyzes and characterizes the Bayes and minimax criteria as extreme poles to deal with (state) uncertainty and develops modern alternatives in the context of complex uncertainty (ambiguity). In the second part, an overview of other decision-theoretic topics is given, also introducing to current research in decision theory.

The enrollment key is: A/DT23

Enrolment Key

SDS-Methods-23

Participants in this course will be automatically added based on the seminar assignments done by Dr. Schollmeyer.

If you have questions, write to ruegamer@stat.uni-muenchen.de

Seminar degree module

Content

Areal data is a data format in which point observations are aggregated over subregions of a predefined space. These subregions are non-overlapping and make up the entire space. This format is predominately common in medical research and one of the central formats in which, for example, data on Corona infections and hospitalizations were available. Due to the loss of information on the specific point of observation, the estimation of the spatial correlation can become a bit trickier than in spatial point processes. In recent years, especially during the pandemic, working with this type of data has become more relevant for data scientists and statisticians not only due to the relevance of their context but also due to somewhat recent advancements in research on network or graph theory, which allowed research on the statistical methods designed to work with this data more diverse.

Schedule

Type	Date	Location
Initial Meeting	May 2nd 9:00-12:30	Seminar Room 144.      Ludwigstr. 33

Dates:

• The enrolment key is: "rademacher"

Schedule

Enrolment Key:  ectheory23

• Der Einschreibeschlüssel lautet: "linalg"

Termine

• Vorlesung & Übung: Freitag, 09 - 12 c.t.

Einschreibeschlüssel

• Der Einschreibeschlüssel lautet "miniconda3".