Florence Bockting - Simulation-Based Prior Knowledge Elicitation for Parametric Bayesian Models

A central characteristic of Bayesian statistics is the ability to consistently incorporate prior knowledge into various modeling processes. In our work, we focus on translating domain expert knowledge into corresponding prior distributions over model parameters, a process known as prior elicitation. Expert knowledge can manifest itself in diverse formats, including information about raw data, summary statistics, or model parameters. A major challenge for existing elicitation methods is how to effectively utilize all of these different formats in order to formulate prior distributions that align with the expert’s expectations, regardless of the model structure. To address these challenges, we develop a simulation-based elicitation method that can learn the hyperparameters of potentially any parametric prior distribution from a wide spectrum of expert knowledge using stochastic gradient descent. We validate the effectiveness and robustness of our elicitation method in representative case studies ranging from (generalized) linear models to hierarchical models. Our method is largely independent of the underlying model structure and adaptable to various elicitation techniques, including quantile-based, moment-based, and histogram-based methods.

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Powerful, thanks for sharing ❤