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.
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Combining Mechanistic Models and Data Analysis
Produktdetaljer
ISBN
9783031731648
Publisert
2024
Utgiver
Vendor
Springer
Språk
Product language
Engelsk
Format
Product format
Digital bok
Forfatter