03459nam a22005295i 4500 978-3-031-73164-8 DE-He213 20251030114619.0 cr nn 008mamaa 241215s2024 sz | s |||| 0|eng d 9783031731648 978-3-031-73164-8 10.1007/978-3-031-73164-8 doi TA345-345.5 UN bicssc COM018000 bisacsh UN thema Altmann, Eduardo G. author. aut http://id.loc.gov/vocabulary/relators/aut 88979 Statistical Laws in Complex Systems [electronic resource] : Combining Mechanistic Models and Data Analysis / by Eduardo G. Altmann. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XIII, 140 p. 30 illus., 29 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Understanding Complex Systems, 1860-0840 Introduction -- Examples of statistical laws -- From data to laws -- Synthesis statistical laws in context -- Datasets and Codes. This book provides a unifying approach to the study of statistical laws, critically evaluating their role in the theoretical understanding of complex systems and the different data-analysis methods used to evaluate them. Statistical laws describe regular patterns observed in diverse scientific domains, ranging from the magnitude of earthquakes (Gutenberg-Richter law) and metabolic rates in organisms (Kleiber's law), to the frequency distribution of words in texts (Zipf's and Herdan-Heaps' laws), and productivity metrics of cities (urban scaling laws). The origins of these laws, their empirical validity, and the insights they provide into underlying systems have been subjects of scientific inquiry for centuries. Through a historical review and a unified analysis, this book argues that the persistent controversies on the validity of statistical laws are predominantly rooted not in novel empirical findings but in the discordance among data-analysis techniques, mechanistic models, and the interpretations of statistical laws. Starting with simple examples and progressing to more advanced time-series and statistical methods, this book and its accompanying repository provide comprehensive material for researchers interested in analyzing data, testing and comparing different laws, and interpreting results in both existing and new datasets. Engineering Data processing. Dynamics. 65524 Nonlinear theories. 65525 Data Engineering. 66059 Applied Dynamical Systems. 65530 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031731631 Printed edition: 9783031731655 Printed edition: 9783031731662 Understanding Complex Systems, 1860-0840 67707 https://doi.org/10.1007/978-3-031-73164-8 ZDB-2-INR ZDB-2-SXIT 65736 65736