论文标题
实验室实验中的顺序决策的最佳学习
Optimal Learning for Sequential Decisions in Laboratory Experimentation
论文作者
论文摘要
物理，生物学和医学科学中的发现过程可能会慢慢慢。大多数实验失败了，从研究开始到新进步的时间可以跨越20年。本教程的目的是为实验科学家提供决策科学的基础。本文使用作者的经验得出的数值示例，描述了任何实验性学习问题的基本要素。它强调了信仰模型的重要作用，不仅包括先前研究，先前的实验和科学专业知识提供的关系的最佳估计，还包括这些关系中的不确定性。我们介绍了学习政策的概念，并审查了政策的主要类别。然后，我们引入了一种称为知识梯度的策略，该策略最大化了每个实验中信息的价值。我们提出了减少不确定性的重要性，并为不同的信念模型说明了这一过程。
The process of discovery in the physical, biological and medical sciences can be painstakingly slow. Most experiments fail, and the time from initiation of research until a new advance reaches commercial production can span 20 years. This tutorial is aimed to provide experimental scientists with a foundation in the science of making decisions. Using numerical examples drawn from the experiences of the authors, the article describes the fundamental elements of any experimental learning problem. It emphasizes the important role of belief models, which include not only the best estimate of relationships provided by prior research, previous experiments and scientific expertise, but also the uncertainty in these relationships. We introduce the concept of a learning policy, and review the major categories of policies. We then introduce a policy, known as the knowledge gradient, that maximizes the value of information from each experiment. We bring out the importance of reducing uncertainty, and illustrate this process for different belief models.
