Early Career Researcher Workshop: Introduction to Bayesian Inference: A Hands-on Workshop for Early-career Scientists
Bayesian methods are increasingly common across the empirical sciences, but getting started can be daunting. This two-day workshop introduces Bayesian inference for researchers who work quantitatively and would like to add Bayesian tools to their repertoire. No prior experience with Bayesian statistics is required.
- https://www.uni-giessen.de/de/fbz/ggn/events/workshops/ecr_bi
- Early Career Researcher Workshop: Introduction to Bayesian Inference: A Hands-on Workshop for Early-career Scientists
- 2026-09-17T10:00:00+02:00
- 2026-09-17T16:00:00+02:00
- Bayesian methods are increasingly common across the empirical sciences, but getting started can be daunting. This two-day workshop introduces Bayesian inference for researchers who work quantitatively and would like to add Bayesian tools to their repertoire. No prior experience with Bayesian statistics is required.
17.09.2026 von 10:00 bis 16:00 (Europe/Berlin / UTC200)
17.09.2026 von 10:00 bis 16:00
18.09.2026 von 10:00 bis 16:00
Ze-S3, Senckenbergstraße 03
Überblick
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Beschreibung des Workshops
Both workshop days combine conceptual input with hands-on work in JASP and in R. JASP is a free, open-source statistics program with a graphical interface. Day 1 builds the foundations: what a posterior distribution is, where priors come from and how much they matter, and how Bayesian inferences differ from frequentist ones. Participants fit the basic analyses themselves and see the benefits of the Bayesian framework in action. Peer exchange gives space to relate the material to participants' own research. Day 2 moves to more involved topics: Model comparison/averaging using a regression example, and how to check and report results. Participants work through example datasets from different empirical fields. The day closes with an introduction to R-based Bayesian tools for models beyond the graphical interface of JASP.
Learning objectives — Participants will learn:
- What a posterior distribution represents and how prior, likelihood, and data combine to produce it
- How Bayesian inference differs from frequentist inference
- How to choose priors and assess how sensitive their conclusions are to that choice
- To fit and interpret standard Bayesian analyses in JASP
- How to check whether a model has converged and whether it describes the data adequately
- Report a Bayesian analysis in a form suitable for publication
- Know which tools to turn to for models beyond the scope of the workshop
Recommended prior knowledge
Familiarity with standard quantitative methods (t-tests, correlation, regression) from applied use. No prior experience with Bayesian statistics required. Part of Day 2 uses R, prior R experience is not strictly needed and all code is provided.
Software
JASP (free and open source) for the hands-on work on both days. On day 2 we will be using R in RStudio. Instructions for installing the required R-packages will be sent to registered participants in advance of the workshop.
Preparation
· Install JASP before the workshop: https://jasp-stats.org/download
· Install R and RStudio
· Bring a laptop
Anmeldung
Die Anmeldung wird bald freigeschaltet.