PhD Student Position on prior specification at the University of Stuttgart, Germany

At the Cluster of Excellence SimTech, University of Stuttgart, Germany, I am currently looking for a PhD Student to work with me on the fully funded project “Simulation-Based Prior Distributions for Bayesian Models”.

Below, you can find a high-level description of the project:

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. Our methods will be practically implemented via normalizing flows using invertible neural networks. Moreover, they will enable amortized prior inference such that a prior based on newly provided expert knowledge can be derived without any additional training of the networks. To that end, 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. This project thus offers a lot of potential for interdisciplinary collaborations within SimTech and beyond.

For more details about the position, please see stellenwerk

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