Probabilistic Modeling and Bayesian Data Analysis in Experimental Semantics and Pragmatics

 

 

Language and Computation Courses

Advanced Course

Probabilistic Modeling and Bayesian Data Analysis in Experimental Semantics and Pragmatics,
Michael Franke (University of Tübingen, Germany) and Michael Henry Tessler (Stanford University, USA)

Week 2, 14:00 – 15:30, Room 272, Floor 3

 

Course website / syllabus

Experimental approaches to theoretical questions in semantics and pragmatics are booming. Some see an ​empirical turn in progress. A welcome enrichment it may be, but the unification of rich theoretical work and novel experimental data brings new conceptual and practical problems: how do established theoretical notions lead to empirically testable predictions and what can we learn from experimental data about theoretical variables of interest? This course addresses these questions by introducing theory-driven probabilistic modeling in connection with Bayesian data analysis as a helpful set of tools to learn from observational data through the lens of a theoretical model. We will introduce the basics of Bayesian data analysis and probabilistic modeling through a series of concrete case studies in natural language semantics and pragmatics.