Data-driven statistical modeling plays a crucial role in almost all quantitative sciences. Despite continuous increases in the amount of available data, the addition of further information sources, such as expert knowledge, often remains an irreplaceable part of setting up high-fidelity models.
Grounded in probability theory, Bayesian statistics provides a principled approach to including expert knowledge in the form of prior distributions, a process called prior elicitation. However, prior elicitation for high-dimensional Bayesian models is infeasible with existing methods due to practical and computational challenges.
With the goal of solving these challenges, we propose to develop simulation-based priors for high-dimensional Bayesian models that allow to incorporate prior information elicited on any model-implied quantities. We expect the developed methods to have a major impact on all fields applying probabilistic modeling by making the use of expert knowledge practical, robust, and computationally feasible.
Project Members: Florence Bockting
Funders: TU Dortmund University