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Bayesian Experimental Design: KL Divergence vs. Wasserstein Distance

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A new paper published on arXiv explores the use of Bayesian experimental design (BED) for calibrating model discrepancies. The research compares Kullback-Leibler (KL) divergence and Wasserstein distance as utility functions within BED. Findings suggest that KL divergence offers faster convergence when model discrepancies are absent, while Wasserstein metrics provide more robust results in the presence of non-negligible model discrepancies, offering practical guidance for selecting appropriate criteria. AI

RANK_REASON The cluster contains a research paper detailing a comparison of two methods for Bayesian experimental design. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Bayesian Experimental Design: KL Divergence vs. Wasserstein Distance

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The cluster contains a research paper detailing a comparison of two methods for Bayesian experimental design. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Huchen Yang, Xinghao Dong, Jin-Long Wu ·

    Bayesian Experimental Design for Model Discrepancy Calibration: A Rivalry between Kullback--Leibler Divergence and Wasserstein Distance

    arXiv:2601.16425v2 Announce Type: replace Abstract: Designing experiments that systematically gather data from complex physical systems is central to accelerating scientific discovery. While Bayesian experimental design (BED) provides a principled, information-based framework tha…

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