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。

贝叶斯最优实验设计中的度量标准在模型不准确性下的应用

Metrics for Bayesian Optimal Experiment Design under Model  Misspecification

A visual hard attention model actively selects and observes a sequence of
subregions in an image to make a prediction. The majority of hard attention
models determine the attention-worthy regions by first analyzing a complete
image. However, it may be the case that the entire image is not available
initially but instead sensed gradually through a series of partial
observations. In this paper, we design an efficient hard attention model for
classifying such sequentially observed scenes. The presented model never
observes an image completely. To select informative regions under partial
observability, the model uses Bayesian Optimal Experiment Design. First, it
synthesizes the features of the unobserved regions based on the already
observed regions. Then, it uses the predicted features to estimate the expected
information gain (EIG) attained, should various regions be attended. Finally,
the model attends to the actual content on the location where the EIG mentioned
above is maximum. The model uses a) a recurrent feature aggregator to maintain
a recurrent state, b) a linear classifier to predict the class label, c) a
Partial variational autoencoder to predict the features of unobserved regions.
We use normalizing flows in Partial VAE to handle multi-modality in the
feature-synthesis problem. We train our model using a differentiable objective
and test it on five datasets. Our model gains 2-10% higher accuracy than the
baseline models when both have seen only a couple of glimpses.

本文阐述了一种利用 Bayesian Optimal Experiment Design 来选择信息区域的视觉 Hard Attention Model。通过合成未观察到的区域的特征来预测信息增益（EIG），并在 EIG 最大的位置上实际观察内容。这种模型使用递归特征聚合器维护递归状态，使用线性分类器来预测类别标签，并使用部分变分自编码器来预测未观察到区域的特征。我们使用可微分的目标函数对模型进行训练，并在五个数据集上进行测试。与基线模型相比，当两者只看到几个方向时，我们的模型获得了 2-10％更高的准确度。