Recent developments in simulation-based amortized inference have ushered in new possibilities for conducting principled Bayesian analysis. The simulation-based approach unlocks the potential of complex models whose likelihoods or priors are not analytically tractable. Amortized approaches make the required computations relatively fast, thus allowing for the deployment of intricate models in scenarios that were hitherto deemed unfeasible or inconvenient. Nevertheless, the novelty of this approach poses a challenge, as its widespread adoption hinges on the availability of user-friendly documentation and resources that simplify entry into the field, as well as empirical examples that validate the method’s usefulness for the practical researchers.

In this project, our emphasis is on applications within cognitive modeling and developmental psychology. We focus on how simulation-based amortized inference can address important challenges within the field, not only during the data analysis phase but also in the planning and execution of studies and experiments. As a by-product we will generate tutorials and educational materials providing gentle introductions into the topic. This project also aims to lay the foundations for integrating simulation-based amortized inference with popular statistical software packages used by practitioners who may not have extensive coding skills, thereby broadening the scope of users benefiting from its advantages.

Project Members: Šimon Kucharský

Förderung: TU Dortmund