摘要翻译：
考虑了由未知确定性参数θ推论确定信号处理系统内在质量的问题。虽然Fisher信息测度F(\theta)$是解决此类问题的经典工具，但在各种情况下，直接计算信息测度可能变得困难。在非线性测量系统的估计理论性能分析中，似然函数的形式使信息测度f(θ）的计算具有挑战性。在没有统计系统模型的封闭形式表达式的情况下，$f(\theta)$的解析推导根本不可能。基于Cauchy-Schwarz不等式，我们导出了一个替代信息测度$S(\theta)$。它给出了Fisher信息$f(\theta)$的一个下界，并具有用系统模型的均值、方差、偏度和峰度来估计的性质。这些实体通常表现出良好的数学可处理性，或者可以在校准的设置中通过真实世界的测量以低复杂度确定。通过各种例子，我们证明$S(\theta)$为$F(\theta)$提供了一个很好的保守近似，并概述了不同的估计理论问题，其中所给出的信息界被证明是有用的。
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英文标题：
《A Pessimistic Approximation for the Fisher Information Measure》
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作者：
Manuel Stein and Josef A. Nossek
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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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：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
  The problem of determining the intrinsic quality of a signal processing system with respect to the inference of an unknown deterministic parameter $\theta$ is considered. While the Fisher information measure $F(\theta)$ forms a classical tool for such a problem, direct computation of the information measure can become difficult in various situations. For the estimation theoretic performance analysis of nonlinear measurement systems, the form of the likelihood function can make the calculation of the information measure $F(\theta)$ challenging. In situations where no closed-form expression of the statistical system model is available, the analytical derivation of $F(\theta)$ is not possible at all. Based on the Cauchy-Schwarz inequality, we derive an alternative information measure $S(\theta)$. It provides a lower bound on the Fisher information $F(\theta)$ and has the property of being evaluated with the mean, the variance, the skewness and the kurtosis of the system model at hand. These entities usually exhibit good mathematical tractability or can be determined at low-complexity by real-world measurements in a calibrated setup. With various examples, we show that $S(\theta)$ provides a good conservative approximation for $F(\theta)$ and outline different estimation theoretic problems where the presented information bound turns out to be useful.
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PDF链接：
https://arxiv.org/pdf/1508.03878