Optimal Learning
Optimal Learning
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DOI：
10.1287/educ.1080.0039
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Warrren B Powell;P. Frazier
中科院分区：
文献类型：
--
作者：
Warrren B Powell;P. Frazier
最佳学习解决了有效地收集信息以做出决策的问题。这些问题既出现在离线设置(进行一系列测量，然后做出决定)和在线设置(做出决定的过程导致观察结果，从而改变对未来观察的信念分布)中。最优学习主要是在观察或测量昂贵的应用中的问题。其中包括昂贵的模拟(一次观察可能需要一天或更长时间)、实验室科学(在实验室测试药物化合物)和现场实验(在建筑中测试一种新的节能技术)。本教程介绍了这个问题领域，涵盖了学习问题的重要方面，并介绍了一系列收集信息的策略。
Optimal learning addresses the problem of efficiently collecting information with which to make decisions. These problems arise in both offline settings (making a series of measurements, after which a decision is made) and online settings (the process of making a decision results in observations that change the distribution of belief about future observations). Optimal learning is an issue primarily in applications where observations or measurements are expensive. These include expensive simulations (where a single observation might take a day or more), laboratory sciences (testing a drug compound in a lab), and field experiments (testing a new energy saving technology in a building). This tutorial provides an introduction to this problem area, covering important dimensions of a learning problem and introducing a range of policies for collecting information.