Abstract Online experiments and specifically A/B testing are commonly used to identify whether a proposed change to a web page is in fact an effective one. This study focuses on basic settings in which a binary outcome is obtained from each user who visits the website and the probability of a response may be affected by numerous factors. We use Bayesian probit regression to model the factor effects and combine elements from traditional two-level factorial experiments and multiarmed bandits to construct sequential designs that embed attractive features of estimation and exploitation.

中文翻译：

---

在线实验的因子设计

摘要 在线实验，特别是 A/B 测试通常用于确定对网页的提议更改是否实际上是有效的。本研究侧重于基本设置，其中从访问网站的每个用户获得二元结果，并且响应的可能性可能受多种因素影响。我们使用贝叶斯概率回归对因子效应进行建模，并结合来自传统两级因子实验和多臂老虎机的元素来构建序列设计，这些设计嵌入了有吸引力的估计和开发特征。