|
|
Einrichtungen >> Fakultät Humanwissenschaften >> Institut für Psychologie >> Lehrstuhl für Allgemeine Psychologie und Methodenlehre >>
|
Bayesian Statistics for Masters (Bayesian Stats)- Dozent/in
- Dr. Alexander Pastukhov
- Angaben
- Seminar
Rein Präsenz
2 SWS, Unterrichtssprache Englisch
Zeit und Ort: Mo 12:00 - 14:00, MG2/01.09
- Voraussetzungen / Organisatorisches
- Some Bachelor level knowledge R is beneficial, but no prior knowledge beyond high school algebra is required. For Ba / Ma Psychology only!
- Inhalt
- 🔢 Want to move beyond rote statistical tests and truly understand the models behind them? This seminar is designed to develop your intuition and practical skills in Bayesian statistics, enabling you to construct, interpret, and critically evaluate statistical models from the ground up.
We will start with probability as counting, progress to simple linear models, and advance to multilevel models—the backbone of statistical inference. Along the way, we’ll uncover how seemingly different tests (t-tests, ANOVA, ANCOVA, Pearson correlation, and more) all stem from the same underlying statistical framework.
🎯 Learning Goals
By the end of this course, you will:
Develop a deep understanding of Bayesian statistical models and their causal implications.
Learn to build and customize models for any research question—moving beyond off-the-shelf statistical tests.
Gain hands-on experience in designing, interpreting, and evaluating models using causal inference tools and information criteria.
Build confidence in applying Bayesian approaches to real-world research problems and complex data structures.
🛠️ Course Methodology
This seminar assumes no prior background in Bayesian statistics but moves quickly into advanced topics relevant to graduate-level research. We will follow Statistical Rethinking by Richard McElreath—an intuitive and rigorous introduction to Bayesian modeling with clear explanations, real-world examples, and R-based applications. Each week, we will:
📖 Read and discuss a chapter from Statistical Rethinking
💻 Implement models in R and Stan
🧠 Analyze case studies and debunk statistical misconceptions
🔎 Explore advanced topics like spurious associations, causal inference, and power analysis
This course is ideal for students who want to move beyond black-box statistics and develop a strong conceptual and practical foundation in Bayesian data analysis.
🚀 Join us in rethinking statistics—one model at a time!
- Empfohlene Literatur
- "Statistical Rethinking: A Bayesian Course with Examples in R and Stan" by Richard McElreath https://www.oreilly.com/library/view/statistical-rethinking/9781482253481/
- Englischsprachige Informationen:
- Title:
- Bayesian Statistics (Masters)
- Credits: 3
- Prerequisites
- Some Bachelor level knowledge R is beneficial, but no prior knowledge beyond high school algebra is required. For Ba / Ma Psychology only!
- Contents
- The purpose of this seminar, is to build your intuition and understanding of (Bayesian) statistics from ground up, starting with a concept of probability as counting, continuing to simple linear models and advancing to more complicated multilevel linear models. The linear models that we study underpin all classic statistical test: t-test, ANOVA, rm ANOVA, ANCOVA, MANOVA, Pearson correlation, etc. You will learn about their simple common structure, understand how to design such models by hand (much simpler than you think), and, most importantly how to interpret and evaluate these models (much harder than you think) using causal calculus tools and information criteria.
Learning Goals: In this seminar, you will learn how to build a statistical model from the ground up with the goal of being able to build a customized model for any statistical problem and analysis. After this course you will understand that a linear regression, a T-test, an ANOVA, or an ANOCOVA all refer to the same simple linear model that you can build yourself. The aim is to make sure that you will know exactly what your analysis does and why you are doing it in this way.
Course Method: This seminar assumes no prior knowledge on your part. We will start with a basic concept of probability-as-counting and proceed to understanding what statistical models are and how to build them. Over the course of the seminar, we will gradually move forward to more advanced topics learning how to identify spurious associations, infer causality, evaluate models, or perform power analysis. Forming a book club we will read "Statistical Rethinking" by Richard McElrath. It is an excellent introductory statistics book that explain even most intimidating topics very clearly, links all seemingly discrepant topics together, and has plenty of examples in R. We will read one chapter every week, practice build models, and discuss the topics and questions during the seminar.
- Literature
- "Statistical Rethinking: A Bayesian Course with Examples in R and Stan" by Richard McElreath https://www.oreilly.com/library/view/statistical-rethinking/9781482253481/
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 12
Hinweis für Web-Redakteure:
Wenn Sie auf Ihren Webseiten einen Link zu dieser Lehrveranstaltung setzen möchten, verwenden Sie bitte einen der folgenden Links:Link zur eigenständigen Verwendung Link zur Verwendung in Typo3
|
 |
|
|
UnivIS ist ein Produkt der Config eG, Buckenhof |
|
|