Introduction: The most basic aspect of modern engineering is the design of operators to act on physical systems in an optimal manner relative to a desired objective – for instance, designing a con-trol policy to autonomously direct a system or designing a classifier to make decisions regarding the sys-tem. These kinds of problems appear in biomedical science, where physical models are created with the intention of using them to design tools for diagnosis, prognosis, and therapy. Methods: In the classical paradigm, our knowledge regarding the model is certain; however, in practice, especially with complex systems, our knowledge is uncertain and operators must be designed while tak-ing this uncertainty into account. The related concepts of intrinsically Bayesian robust operators and op-timal Bayesian operators treat operator design under uncertainty. An objective-based experimental de-sign procedure is naturally related to operator design: We would like to perform an experiment that max-imally reduces our uncertainty as it pertains to our objective. Results & Discussion: This paper provides a nonmathematical review of optimal Bayesian operators directed at biomedical scientists. It considers two applications important to genomics, structural interven-tion in gene regulatory networks and classification. Conclusion: The salient point regarding intrinsically Bayesian operators is that uncertainty is quantified relative to the scientific model, and the prior distribution is on the parameters of this model. Optimization has direct physical (biological) meaning. This is opposed to the common method of placing prior distri-butions on the parameters of the operator, in which case there is a scientific gap between operator design and the phenomena.

中文翻译：

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用于基因组学的不确定科学模型的最优算子和实验设计的非数学回顾

简介：现代工程的最基本方面是设计操作员以相对于预期目标以最佳方式对物理系统采取行动——例如，设计控制策略以自主引导系统或设计分类器以做出有关系统。这些类型的问题出现在生物医学科学中，其中创建物理模型的目的是使用它们来设计用于诊断、预后和治疗的工具。方法：在经典范式中，我们对模型的了解是确定的；然而，在实践中，尤其是对于复杂系统，我们的知识是不确定的，因此在设计算子时必须考虑到这种不确定性。本质贝叶斯鲁棒算子和最优贝叶斯算子的相关概念是在不确定性下处理算子设计。基于目标的实验设计程序自然与操作员设计相关：我们希望进行一项实验，最大限度地减少与我们的目标相关的不确定性。结果与讨论：本文对针对生物医学科学家的最佳贝叶斯算子进行了非数学综述。它考虑了基因组学的两个重要应用，基因调控网络的结构干预和分类。结论：本质贝叶斯算子的重点是不确定性是相对于科学模型进行量化的，并且先验分布在该模型的参数上。优化具有直接的物理（生物）意义。这与将先验分布放在算子参数上的常用方法相反，