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.

本文介绍了一种扩展的贝叶斯决策实验设计框架，不仅考虑了传统的期望信息增益准则，还引入了期望一般信息增益和期望鉴别信息等准则，以量化模型差异的稳健性和实验检测模型差异的能力。通过应用于一个带有模型差异的弹性质量阻尼系统和 F-16 模型的场景中，展示了该框架的功能。