Lasse Elsemüller, M.Sc.
Hauptstr. 47, Room F023
69117 Heidelberg
E-mail: lasse.elsemuellerpsychologie.uni-heidelberg.de
ORCID iD: 0000-0003-0368-720X
Primary Advisor: Prof. Dr. Andreas Voss (University of Heidelberg)
Additional Supervisors: Stefan Radev (external), Edgar Erdfelder, Andrea Kiesel
Dissertation Proposal: Bridging the Gap between Amortized Bayesian Inference and Cognitive Modeling
Publications
Elsemüller, L., Schnuerch, M., Bürkner, P.-C., & Radev, S. T. (2024). A deep learning method for comparing Bayesian hierarchical models. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000645
Radev, S. T., Schmitt, M., Schumacher, L., Elsemüller, L., Pratz, V., Schälte, Y., Köthe, U., & Bürkner, P.-C. (2023). Bayesflow: Amortized bayesian workflows with neural networks. Journal of Open Source Software, 8(89), Article 5702. https://doi.org/10.21105/joss.05702
Talks
Elsemüller, L., Schnuerch, M., Bürkner, P.-C., & Radev, S. T. (2023, July). Comparing Bayesian hierarchical models: A deep learning method with cognitive applications. Talk presented at the Joint 56th Meeting of the Society for Mathematical Psychology, 21st International Conference on Cognitive Modeling, and 52nd Meeting of the European Mathematical Psychology Group (MathPsych / ICCM / EMPG). Amsterdam, the Netherlands.
Elsemüller, L., Schnuerch, M., Bürkner, P. C., & Radev, S. T. (2023, March). Comparing Bayesian Hierarchical Models of Cognition via Deep Learning. Talk presented at the 65th Conference of Experimental Psychologists (Tagung experimentell arbeitender Psycholog:innen, TeaP), Trier, Germany.