Data Fitting and Uncertainty: A practical introduction to weighted least squares and beyond
Data Fitting and Uncertainty: A practical introduction to weighted least squares and beyond
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发表时间：
2010-09
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通讯作者：
T. Strutz
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作者：
T. Strutz
数据拟合这一学科跨越了许多学科，特别是那些传统上涉及统计学的学科，如物理、数学、工程、生物学、经济或心理学，但也包括计算机视觉等较新的领域。本书面向对最小二乘近似方法数据拟合感兴趣，但在该领域没有或仅有有限知识的工程师和计算机科学家或相应的本科生。有经验的读者会在其中发现新的想法，或者可能会欣赏这本书作为一本有用的参考书。熟悉基本的线性代数是有帮助的，但不是必需的，因为本书包含一个独立的介绍，并以逻辑且易于理解的方式介绍了该方法。本文的主要目标是解释通过最小二乘法进行数据拟合的工作原理。读者会发现本书的重点是实践问题，而不是理论问题。此外，本书还使读者能够在对几个示例的综合讨论的基础上，设计自己的具有特定于应用程序的模型功能的软件实现。该文本附有 ANSI-C 的工作源代码，用于拟合加权最小二乘法（包括异常值检测）。
The subject of data fitting bridges many disciplines, especially those traditionally dealing with statistics like physics, mathematics, engineering, biology, economy, or psychology, but also more recent fields like computer vision. This book addresses itself to engineers and computer scientists or corresponding undergraduates who are interested in data fitting by the method of least squares approximation, but have no or only limited pre-knowledge in this field. Experienced readers will find in it new ideas or might appreciate the book as a useful work of reference. Familiarity with basic linear algebra is helpful though not essential as the book includes a self-contained introduction and presents the method in a logical and accessible fashion. The primary goal of the text is to explain how data fitting via least squares works. The reader will find that the emphasis of the book is on practical matters, not on theoretical problems. In addition, the book enables the reader to design own software implementations with application-specific model functions based on the comprehensive discussion of several examples. The text is accompanied with working source code in ANSI-C for fitting with weighted least squares including outlier detection.