论文标题
关于贝叶斯D型宾至如归标准的简短说明
A brief note on the Bayesian D-optimality criterion
论文作者
论文摘要
我们考虑使用高斯先验和加斯高斯噪声模型的有限维贝叶斯线性逆问题。本注的目的是提出一个简单的推导，即众所周知的事实是，解决贝叶斯D-最佳的实验设计问题，即最大化预期信息增益，相当于最大程度地减少后协方差操作员的数目。我们专注于有限维的逆问题。但是，该介绍是一般性的，以促进扩展到无限二维逆问题。
We consider finite-dimensional Bayesian linear inverse problems with Gaussian priors and additive Gaussian noise models. The goal of this note is to present a simple derivation of the well-known fact that solving the Bayesian D-optimal experimental design problem, i.e., maximizing the expected information gain, is equivalent to minimizing the log-determinant of posterior covariance operator. We focus on finite-dimensional inverse problems. However, the presentation is kept generic to facilitate extensions to infinite-dimensional inverse problems.
