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＜電子ブック＞
Identifiability and Regression Analysis of Biological Systems Models : Statistical and Mathematical Foundations and R Scripts / by Paola Lecca
(SpringerBriefs in Statistics. ISSN:21915458)

版	1st ed. 2020.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2020
本文言語	英語
大きさ	X, 82 p. 13 illus., 8 illus. in color : online resource
著者標目	*Lecca, Paola author SpringerLink (Online service)
件　名	LCSH:Biometry LCSH:Bioinformatics LCSH:Statistics  LCSH:Biomathematics FREE:Biostatistics FREE:Computational and Systems Biology FREE:Statistical Theory and Methods FREE:Mathematical and Computational Biology FREE:Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
一般注記	1 Complex systems and sets of data -- 2 Dynamic models -- 3 Model identifiability -- 4 Relationships between phenomena -- 5 Codes 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 bioinformatics, bioengineering, systems biology, biophysics, biochemistry, the environmental sciences and experimental physics. Readers should be familiar with the fundamentals of probability and statistics (as provided in first-year university courses) and a basic grasp of R HTTP:URL=https://doi.org/10.1007/978-3-030-41255-5

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