发帖
雷达卡
摘要翻译：
不确定度的平均目标成本(MOCU)量化了在不确定系统中使用最优算子而不是使用对特定系统最优的算子的性能成本。基于MOCU的实验设计选择一个实验来最大限度地降低MOCU，从而获得对操作目标影响的不确定性的最大降低。用于寻找最优系统算子的原始公式，其中最优性是关于成本函数，如均方误差；控制不确定性类的先验分布与底层物理系统直接相关。这里我们提供了一个广义的MOCU和相应的实验设计。然后，我们演示了当存在实验误差时，这个新的公式如何包括作为特例的基于MOCU的实验设计方法，这些方法是为材料科学和基因组网络开发的。最重要的是，我们证明了经典的知识梯度和高效的全局优化实验设计过程实际上是基于MOCU的实验设计在其建模假设下的实现。
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英文标题：
《Experimental Design via Generalized Mean Objective Cost of Uncertainty》
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作者：
Shahin Boluki, Xiaoning Qian, Edward R. Dougherty
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
The mean objective cost of uncertainty (MOCU) quantifies the performance cost of using an operator that is optimal across an uncertainty class of systems as opposed to using an operator that is optimal for a particular system. MOCU-based experimental design selects an experiment to maximally reduce MOCU, thereby gaining the greatest reduction of uncertainty impacting the operational objective. The original formulation applied to finding optimal system operators, where optimality is with respect to a cost function, such as mean-square error; and the prior distribution governing the uncertainty class relates directly to the underlying physical system. Here we provide a generalized MOCU and the corresponding experimental design. We then demonstrate how this new formulation includes as special cases MOCU-based experimental design methods developed for materials science and genomic networks when there is experimental error. Most importantly, we show that the classical Knowledge Gradient and Efficient Global Optimization experimental design procedures are actually implementations of MOCU-based experimental design under their modeling assumptions.
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PDF链接：
https://arxiv.org/pdf/1805.01143
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