Hasso-Plattner-Institut25 Jahre HPI
Hasso-Plattner-Institut25 Jahre HPI

Statistical Models (Sommersemester 2021)

Dozent: Prof. Dr. Bernhard Renard (Data Analytics and Computational Statistics) , Dr. Katharina Baum (Data Analytics and Computational Statistics)

Allgemeine Information

  • Semesterwochenstunden: 2
  • ECTS: 3
  • Benotet: Ja
  • Einschreibefrist: 18.03.2021 - 09.04.2021
  • Lehrform: Seminar
  • Belegungsart: Wahlpflichtmodul
  • Lehrsprache: Englisch
  • Maximale Teilnehmerzahl: 6

Studiengänge, Modulgruppen & Module

IT-Systems Engineering MA
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-K Konzepte und Methoden
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-T Techniken und Werkzeuge
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-S Spezialisierung
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-K Konzepte und Methoden
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-T Techniken und Werkzeuge
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-S Spezialisierung
Data Engineering MA
Digital Health MA
Cybersecurity MA


Uncertainty is omnipresent in everyday’s life, it may just start to rain, when we walk out of the door and even the worst soccer team may still win a game some day. In order to rationalize decision making under this uncertainty, we can describe systems in mathematical terms using statistical models. Some statistical models, such as the linear model are commonly applied, but there is a much larger variety of flexible ways to describe systems in reality.

In this seminar, we want to regard and discuss statistical models not only with regard to their motivation, but critically discuss application scenarios on real datasets to see strengths and weaknesses.


Learning objectives

  • You learn to analyze strengths and weaknesses of statistical models
  • You learn to characterize a statistical model and explore its application to real life data (of your choice)
  • You can present a scientific manuscript in this field and lead a discussion


You should have some mathematical background (at least Mathe 1+2 of the ITSE bachelor or comparable) as well as have taken at least one class in statistics.
Good knowledge of English to understand and discuss current literature (the lecture will be given in English, but you can ask questions in German and submit German solutions etc.).


Generalized Linear Models (https://doi.org/10.1007/978-1-4614-8775-3 , chapter 9)

Generalized Linear Mixed Models (https://stats.idre.ucla.edu/other/mult-pkg/introduction-to-generalized-linear-mixed-models/)

Hidden Markov Models (http://ai.stanford.edu/~pabbeel/depth_qual/Rabiner_Juang_hmms.pdf)

Gaussian Process Models (https://doi.org/10.1016/j.jmp.2018.03.001)

Structural Equation Models (https://bmcresnotes.biomedcentral.com/articles/10.1186/1756-0500-3-267)

Lern- und Lehrformen

  • Seminar for master students 
  • Language of instruction: English
  • Maximum number of participants: 6

Topics will be presented in the first Session (April 13th, 2021). For topic assignments, participants will have to write an E-Mail by April 17th, 2020 in which they can give preferences for up to 3 of the presented topics. Then, the topics will be assigned by us. In case of too many applicants, we will decide randomly.
The seminar will be conducted virtually via zoom at least for the start of the semester
Please sign up on moodle (https://hpi.de/friedrich/moodle/course/view.php?id=155) for the course.


In the seminar, each participant will give a presentation about a predefined topic within the research area of truth discovery and write a short report. The final grade consists of the following three parts:

  • Presentation (45%)
  • Written report (35%)
  • Discussion in the seminar sessions (20%)