Our understanding of human speech is increasingly shaped by quantitative data. It is thus of critical importance to evaluate quantitative findings inferentially. This workshop aims at introducing Bayesian inference for the quantification of phonetic data.

Bayesian inference more closely answers the research questions we ask; it is much more flexible; and it allows us to run appropriate statistical tests.

Until recently, this framework was technically very involved and represented computational challenges. These challenges have now been overcome, making Bayesian inference conceptually, technically, and computationally feasible for researchers across disciplines.

Do you want to learn Bayesian data analysis?You’ve come to the right place

Why Bayesian?

Flexible Modelling

Robust Inference

Technically feasible

The Workshop

Our workshop introduces the logic of Bayesian inference and contrast it to null-hypothesis-significance-testing. After providing a brief conceptual introduction, the course will walk through a Bayesian statistical analysis using R and the package brms (Bürkner 2017).

We will explain how to set up a Bayesian regression model (including setting appropriate priors), how to test 'hypotheses' (including parameter estimation and Bayes factor), how to interpret the results, how to diagnose model convergence, and how to visualize and report the results. In hands-on exercises, the participants will immediately apply their knowledge to new data sets in R.


The instructors

  • Team Member

    Timo Roettger

    University of Oslo

    Team Member

    Joseph Casillas

    Rutgers University

    Team Member

    Stefano Coretta

    LMU Munich