论文标题
贝叶斯的顺序实验设计，用于具有高斯工艺的部分线性模型的先验
Bayesian Sequential Experimental Design for a Partially Linear Model with a Gaussian Process Prior
论文作者
论文摘要
我们研究了顺序实验设计的问题，以估计具有高斯过程的部分线性模型的参数分量。我们考虑一个积极的学习环境，实验者可以适应地决定要收集哪些数据以有效地实现其目标。实验者的目标可能会有所不同，例如减少分类误差概率或提高估计数据生成过程参数的准确性。这项研究旨在提高估计部分线性模型的参数成分的准确性。在某些假设下，部分线性模型的参数分量可以视为因果参数，平均治疗效果（ATE）或平均因果效应（ACE）。我们为具有高斯工艺的部分线性模型提出了一种贝叶斯顺序实验设计算法，该模型也被认为是针对ATE或ACE估计的顺序实验设计。我们通过基于合成和半合成数据的数值实验来显示所提出的方法的有效性。
We study the problem of sequential experimental design to estimate the parametric component of a partially linear model with a Gaussian process prior. We consider an active learning setting where an experimenter adaptively decides which data to collect to achieve their goal efficiently. The experimenter's goals may vary, such as reducing the classification error probability or improving the accuracy of estimating the parameters of the data generating process. This study aims to improve the accuracy of estimating the parametric component of a partially linear model. Under some assumptions, the parametric component of a partially linear model can be regarded as a causal parameter, the average treatment effect (ATE) or the average causal effect (ACE). We propose a Bayesian sequential experimental design algorithm for a partially linear model with a Gaussian process prior, which is also considered as a sequential experimental design tailored to the estimation of ATE or ACE. We show the effectiveness of the proposed method through numerical experiments based on synthetic and semi-synthetic data.
