Introduction to Bayesian Parameter Estimation with Stan

This workshop introduces the core principles of Bayesian parameter estimation and illustrates its application with the software Stan. After a short introduction to the definition of likelihood, prior, and posterior distributions, the conceptual foundations of Markov chain Monte Carlo methods are discussed and practiced by fitting simple models with the software Stan.

Please bring your own laptops and make sure to install all necessary software (R, RStudio, Rtools, and the package rstan) as explained on this website: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started

Instructor: Dr. Daniel W. Heck

Daniel Heck is a Postdoctoral Researcher at the DFG Research Training Group “Statistical Modeling in Psychology“. He received his Ph.D. from the University of Mannheim (with an intermediate stay as a visiting researcher in the lab of E.-J. Wagenmakers at the University of Amsterdam). His research focuses on innovations in statistical methods, Bayesian statistics in particular, mathematical modeling of cognitive processes, and judgment and decision-making. His methodological and substantive contributions to the field of psychology have been published in leading journals such as Psychological Review, Cognitive Psychology, Psychometrika, Psychonomic Bulletin & Review, and the Journal of Mathematical Psychology, to name a few. For his achievements, he received several prestigious awards, such as the Heinz Heckhausen Award of the Deutsche Gesellschaft für Psychologie (DGPs) and the Rising Star Award of the Association for Psychological Science (APS).

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