mlsanityData to 14 Oct 2026

Metrics for Bayesian Optimal Experiment Design under Model Misspecification

arXiv 2304.07949 ↗First posted 17 Apr 2023stat.ME cs.LG cs.SYcited by 7

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Abstract (arXiv)

The conventional approach to Bayesian decision-theoretic experiment design involves searching over possible experiments to select a design that maximizes the expected value of a specified utility function. The expectation is over the joint distribution of all unknown variables implied by the statistical model that will be used to analyze the collected data. The utility function defines the objective of the experiment where a common utility function is the information gain. This article introduces an expanded framework for this process, where we go beyond the traditional Expected Information Gain criteria and introduce the Expected General Information Gain which measures robustness to the model discrepancy and Expected Discriminatory Information as a criterion to quantify how well an experiment can detect model discrepancy. The functionality of the framework is showcased through its application to a scenario involving a linearized spring mass damper system and an F-16 model where the model discrepancy is taken into account while doing Bayesian optimal experiment design.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 17 Apr 2023.

Builds on

6 of 19 references.

References · 6

  1. 2014Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing Itcited by 95
  2. 2021On Statistical Bias In Active Learning: How and When To Fix Itcited by 75
  3. 2022Robust Expected Information Gain for Optimal Bayesian Experimental Design Using Ambiguity Setscited by 12
  4. 2019Bayesian decision-theoretic design of experiments under an alternative modelcited by 8
  5. 2022Characterizing the robustness of Bayesian adaptive experimental designs to active learning biascited by 8
  6. 2019Generalizing Information to the Evolution of Rational Beliefcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 4; 2026: 2.

7 papers.

  1. 2023Bayesian Active Learning in the Presence of Nuisance Parameterscited by 6
  2. 2025Active Learning of Model Discrepancy with Bayesian Experimental Designcited by 5
  3. 2025Performance Comparisons of Reinforcement Learning Algorithms for Sequential Experimental Designcited by 3
  4. 2025Bayesian Experimental Design for Model Discrepancy Calibration: An Auto-Differentiable Ensemble Kalman Inversion Approachcited by 2
  5. 2025Representative, Informative, and De-Amplifying: Requirements for Robust Bayesian Active Learning under Model Misspecificationcited by 2
  6. 2026Maximin Robust Bayesian Experimental Designcited by 1
  7. 2026Bayesian Experimental Design for Model Discrepancy Calibration: A Rivalry between Kullback--Leibler Divergence and Wasserstein Distancecited by 0