|Dozierende||Angela Leipold (firstname.lastname@example.org, BeurteilerIn)|
|Inhalt||This workshop will be held by Ladislas Nalborczyk (https://lnalborczyk.github.io/)
What is Bayesian statistical modelling?
The Bayesian approach involves treating each entity (e.g., observed variables, model parameters, missing data) as random variables characterised by probability distributions. In a Bayesian analysis, each unknown entity is assigned an a priori distribution that represents a state of knowledge prior to observing some data. Once the data have been observed, Bayes’ theorem is used to update the a priori distribution into an a posteriori distribution. The a posteriori distribution is the goal of a Bayesian analysis and can be summarised by point values or intervals, and interpreted directly within a coherent probabilistic framework.
This approach differs - both philosophically and in practice - from the traditional frequentist approach, which makes up the majority of available courses. One of the advantages of the Bayesian approach is that it enables the analyst to solve problems that are difficult, if not impossible, for the traditional frequentist approach. In the course of the proposed examples, we’ll realise that even in simple modeling situations, the Bayesian approach enables more natural and flexible probabilistic reasoning than the inferential machinery of the frequentist approach. Bayesian statistical modeling represents an attractive alternative to frequentist approaches in that it offers a coherent probabilistic framework for statistical modeling. The Bayesian approach makes it possible to build and fit complex models while offering intuitive conclusions that incorporate all the uncertainty intrinsic to the inferential process.
|Lernziele||The aim of this course is to introduce you to the Bayesian approach and the brms package. The concepts and tools presented during the course will be illustrated by concrete cases of data analysis. The course is built around the R language and the brms package, an interface to the probabilistic Stan language. At the end of this course, you should be able to build and fit regression models adapted to your problem with brms.|
|Bemerkungen||The workshop is offered by the Graduate School of Psychology.
Maximum number of participants: 12.
1. Currently a PhD candidate in psychology
2. Member MSc Science Track
3. First come - first served
GSP rules for withdrawal, absence, or partial course attendance:
If you cannot attend a workshop that you have signed up for, please cancel your course registration as soon as possible by sending an e-mail to email@example.com. This will allow other PhD stu-dents interested in the course to move up from the waiting list. The GSP strongly values fairness. Af-ter the university’s official registration period has ended, cancelled course will be assessed with NE. Course registrations can be cancelled until one week before the workshops begins. In the case of late cancellations, participants will not be considered for course registration in the next three months of the lecture periods and will be removed from the registration list of any course they have signed up during these three upcoming months of the lecture periods.
If an urgent, unforeseeable, or inevitable event before or during the workshop prevents a participant from attending, a written and substantiated withdrawal request that includes appropriate documenta-tion (e.g. a doctor’s note) must be submitted to firstname.lastname@example.org within two days of the work-shop and without being prompted. The program director decides on the approval of the withdrawal request.
ECTS-points are awarded only for 100% course attendance.
|Teilnahmebedingungen||Doktorat im Bereich Psychologie.
Für Masterstudierende im SWE Science Track: Anmeldung und erfolgreiche Aufnahme in den SWE Science Track.
Certain prerequisites are essential to take part in this course: Familiarity with the basic concepts of inferential statistics (e.g., hypothesis testing, confidence intervals, linear regression). Knowledge of data manipulation in R, elementary objects and calculations in R. Basically, you should already have processed data and fitted a few models in R.
|Einsatz digitaler Medien||kein spezifischer Einsatz|
|Datum||30.11.2023 – 01.12.2023|
|Donnerstag 30.11.2023||14.00-18.00 Uhr||Missionsstrasse 64a, Seminarraum 00.008|
|Freitag 01.12.2023||09.00-12.00 Uhr||Missionsstrasse 64a, Semianrraum 00.010|
Doktorat Psychologie: Empfehlungen (Promotionsfach Psychologie)
Modul: Science Track Sozial-, Wirtschafts- und Entscheidungspsychologie (Masterstudium: Psychologie)
|An-/Abmeldung zur Leistungsüberprüfung||Anm.: Belegen Lehrveranstaltung; Abm.: stornieren|
|Skala||Pass / Fail|
|Wiederholtes Belegen||beliebig wiederholbar|
|Zuständige Fakultät||Fakultät für Psychologie, email@example.com|
|Anbietende Organisationseinheit||Fakultät für Psychologie|