| 書目名稱 | Functional and Shape Data Analysis | | 編輯 | Anuj Srivastava,Eric P. Klassen | | 視頻video | http://file.papertrans.cn/350/349864/349864.mp4 | | 概述 | Presents a complete and detailed exposition on statistical analysis of shapes that includes appendices, background material, and exercises, making this text a self-contained reference.Addresses and ex | | 叢書名稱 | Springer Series in Statistics | | 圖書封面 |  | | 描述 | This textbook for courses on function data analysis and shape data analysis describes how to define, compare, and mathematically represent shapes, with a focus on statistical modeling and inference. It is aimed at graduate students in analysis in statistics, engineering, applied mathematics, neuroscience, biology, bioinformatics, and other related areas. The interdisciplinary nature of the broad range of ideas covered—from introductory theory to algorithmic implementations and some statistical case studies—is meant to familiarize graduate students with an array of tools that are relevant in developing computational solutions for shape and related analyses.These tools, gleaned from geometry, algebra, statistics, and computational science, are traditionally scattered across different courses, departments, and disciplines; .Functional and Shape Data Analysis .offers a unified, comprehensive solution by integrating the registration problem into shape analysis, better preparing graduate students for handling future scientific challenges..Recently, a data-driven and application-oriented focus on shape analysis has been trending. This text offers a self-contained treatment of this new gen | | 出版日期 | Textbook 2016 | | 關(guān)鍵詞 | Riemannian methods; shape analysis; function data analysis; curves; mathematical representations; vector- | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4939-4020-2 | | isbn_softcover | 978-1-4939-8155-7 | | isbn_ebook | 978-1-4939-4020-2Series ISSN 0172-7397 Series E-ISSN 2197-568X | | issn_series | 0172-7397 | | copyright | Springer-Verlag New York 2016 |
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