00774cam2 2200253 450 E60020006893720210414121848.0069107344920101119d1989 |||||ita|0103 baengGB2The adventure of immanencePricetonPricenton University Press1989XV , 225 p24 cm001E6002000689332000 Spinoza and other hereticsITUNISOB20210414RICAUNISOBUNISOB10064980E600200068937M 102 Monografia moderna SBNM100006274-2Si64980acquistopregresso3UNISOBUNISOB20101119081528.020210414121836.0Alfano261340UNISOB03915nam 22006855 450 991048196470332120250318115243.09783030412555303041255510.1007/978-3-030-41255-5(CKB)5300000000003484(DE-He213)978-3-030-41255-5(MiAaPQ)EBC6129282(PPN)243226470(EXLCZ)99530000000000348420200305d2020 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierIdentifiability and Regression Analysis of Biological Systems Models Statistical and Mathematical Foundations and R Scripts /by Paola Lecca1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (X, 82 p. 13 illus., 8 illus. in color.)SpringerBriefs in Statistics,2191-54589783030412548 3030412547 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.SpringerBriefs in Statistics,2191-5458BiometryBioinformaticsStatisticsBiomathematicsStatisticsBiostatisticsComputational and Systems BiologyStatistical Theory and MethodsMathematical and Computational BiologyStatistics in Engineering, Physics, Computer Science, Chemistry and Earth SciencesBiometry.Bioinformatics.Statistics.Biomathematics.Statistics.Biostatistics.Computational and Systems Biology.Statistical Theory and Methods.Mathematical and Computational Biology.Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.572.0727Lecca Paolaauthttp://id.loc.gov/vocabulary/relators/aut985245MiAaPQMiAaPQMiAaPQBOOK9910481964703321Identifiability and Regression Analysis of Biological Systems Models2251851UNINA