Applied Bayesian Statistics (Wintersemester 2020/2021)

Lecturer: Prof. Dr. Bernhard Renard (Data Analytics and Computational Statistics) , Jens-Uwe Ulrich (Data Analytics and Computational Statistics)
Course Website: https://hpi.de/friedrich/moodle/course/view.php?id=106

General Information

• Weekly Hours: 2
• Credits: 3
• Graded: yes
• Enrolment Deadline: 01.10.2020 -20.11.2020
• Teaching Form: Lecture / Project
• Enrolment Type: Compulsory Elective Module
• Course Language: English
• Maximum number of participants: 30

Programs, Module Groups & Modules

Description

In almost all areas of life, large amounts of data are generated, requiring dedicated procedures for data analysis to allow predictions and inference for decision making. Computational statistical methods have evolved to cope with challenges arising from large datasets that are not tractable with traditional approaches, e.g. when the number of possible parameters of a model exceeds the number of observations. At the same time, this wealth of data allows replacing distributional assumptions with data-driven analyses.

In this course, we will dive deeper into the world of Bayesian statistical analysis. We will cover Bayesian inferences and hypothesis testing, sampling algorithms, regression as well as Bayesian models and nets. We will also contrast the Bayesian methods to traditional frequentist approaches.

Learning Objectives:

• Understand concepts and methods of Bayesian statistics
• Ability to statistically analyse big data sets using bayesian methods
• Ability to assess the quality and validity of certain Bayesian methods for a given analysis
• Ability to select, implement and apply appropriate Bayesian methods and algorithms for a given use case

Requirements

• Fundamentals in calculus and vector analysis (at least comparable to the Mathematik I + II lectures in the ITSE Bachelor at HPI)
• Knowledge of key statistical concepts
• Basic knowledge of Python or R programming language or profound skills in a third programming language
• Knowledge of English

Literature

Lawson, A. B., & Lesaffre, E. (2013). Bayesian biostatistics. Wiley.

McElreath, R. (2016). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC Press

Learning

Due to the COVID-19 pandemic, this course will be offered as an Inverted Classroom Lecture. The lecture material will be composed of prerecorded videos, texts and exercises. The students are expected to self study the material as preparation for the in-class session. During in-class sessions we will discuss the concepts and do some exercises and we will use the time to discuss the course projects.

Examination

Students will apply their knowledge to a course project with the intention to select and work on a projects based on the course syllabus. A project typically corresponds to an implementation of an idea or an application of a technology solving a big data problem. 

Students are expected to:

• Write a project proposal (25% of final grade)
• Write a progress report (not graded)
• Do a project talk in class (25% of final grade)
• Produce and submit a professional report on your project (50% of final grade)

Dates

Due to the COVID19 pandemic and the goal to reduce contacts, we will only meet online via Zoom. 

Monday 11:00 - 12:30 @ Zoom online

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