ONE-ARMED BANDIT PROBLEMS WITH COVARIATES
ONE-ARMED BANDIT PROBLEMS WITH COVARIATES
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DOI：
10.1214/aos/1176348382
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发表时间：
1991-12-01
影响因子：
4.5
通讯作者：
SARKAR, J
中科院分区：
文献类型：
--
作者：
SARKAR, J
与Woodroofe一样，我们考虑了两个治疗之间的贝叶斯序贯分配，其中包含协变量。目标是最大化来自无限患者群体的总折扣预期奖励。尽管我们的模型比Woodroofe的模型更通用，但我们能够重复他的主要结果：近视规则是渐进最优的。
As does Woodroofe, we consider a Bayesian sequential allocation between two treatments that incorporates a covariate. The goal is to maximize the total discounted expected reward from an infinite population of patients. Although our model is more general than Woodroofe's, we are able to duplicate his main result: The myopic rule is asymptotically optimal.