Robust Experimental Design via Generalised Bayesian Inference

Abstract:Bayesian optimal experimental design is a principled framework for conducting experiments that leverages Bayesian inference to quantify how much information one can expect to gain from selecting a certain design. However, accurate Bayesian inference relies on the assumption that one's statistical model of the data-generating process is correctly specified. If this assumption is violated, Bayesian methods can lead to poor inference and estimates of information gain. Generalised Bayesian (or Gibbs) inference is a more robust probabilistic inference framework that replaces the likelihood in the Bayesian update by a suitable loss function. In this work, we present Generalised Bayesian Optimal Experimental Design (GBOED), an extension of generalised Bayesian inference to the experimental design setting which achieves robustness in both design and inference. Using a generalisation of Bayesian decision theory, we derive a new acquisition function, the Gibbs expected information gain. Our empirical results demonstrate that GBOED can enhance robustness to outliers and incorrect assumptions about the outcome noise distribution.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2511.07671 [stat.ML]

(or arXiv:2511.07671v2 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2511.07671 Focus to learn more arXiv-issued DOI via DataCite

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