论文标题
参数估计中的自适应实验设计的简化算法
Simplified algorithms for adaptive experiment design in parameter estimation
论文作者
论文摘要
在估计参数模型参数的实验中，贝叶斯实验设计允许根据效用选择测量设置，这是由于建模的测量结果而预测的参数分布的改进。在本文中，我们将基于信息理论的实用程序与三种替代实用程序算法进行比较。在模拟自适应测量中对这些效用替代方案的测试表明，计算速度的改善很大，对测量效率有轻微的影响。
In experiments to estimate parameters of a parametric model, Bayesian experiment design allows measurement settings to be chosen based on utility, which is the predicted improvement of parameter distributions due to modeled measurement results. In this paper we compare information-theory-based utility with three alternative utility algorithms. Tests of these utility alternatives in simulated adaptive measurements demonstrate large improvements in computational speed with slight impacts on measurement efficiency.
