| 書目名稱 | Identifiability and Regression Analysis of Biological Systems Models | | 副標(biāo)題 | Statistical and Math | | 編輯 | Paola Lecca | | 視頻video | http://file.papertrans.cn/461/460812/460812.mp4 | | 概述 | Presents advanced techniques for the identifiability analysis, standard and robust regression analysis of complex dynamical models.Illustrated with a wealth of real-world examples.Provides exercises a | | 叢書名稱 | SpringerBriefs in Statistics | | 圖書封面 |  | | 描述 | .This richly illustrated book presents the objectives of, and the latest techniques for, the identifiability analysis and standard and robust regression analysis of complex dynamical models. The book first provides a definition of complexity in dynamic systems by introducing readers to the concepts of system size, density of interactions, stiff dynamics, and hybrid nature of determination. In turn, it presents the mathematical foundations of and algorithmic procedures for model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best predictor selection..Although the main fields of application discussed in the book are biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as physics and the environmental sciences. Readers will learn how to deal with problems such as determining the identifiability conditions, searching for an identifiable model, and conducting theirown regression analysis and diagnostics without supervision. . .Featuring a wealth of real-world examples, exercises, and codes in R, the book addresses the needs of doctoral students and researchers in bioinformatic | | 出版日期 | Book 20201st edition | | 關(guān)鍵詞 | Model identifiability; Regression analysis; Stiff dynamics; Non-linear dynamics; Parameter inference; Sel | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-41255-5 | | isbn_ebook | 978-3-030-41255-5Series ISSN 2191-544X Series E-ISSN 2191-5458 | | issn_series | 2191-544X | | copyright | The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 |
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