國立虎尾科技大學 |

Statistical laws in complex systems = combining mechanistic models and data analysis /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Statistical laws in complex systems/ by Eduardo G. Altmann.
其他題名:	combining mechanistic models and data analysis /
作者:	Altmann, Eduardo G.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xiii, 140 p. :ill. (chiefly color), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	System analysis - Statistical methods. -
電子資源:	https://doi.org/10.1007/978-3-031-73164-8
ISBN:	9783031731648

Statistical laws in complex systems = combining mechanistic models and data analysis /
Altmann, Eduardo G.

Statistical laws in complex systemscombining mechanistic models and data analysis /[electronic resource] :by Eduardo G. Altmann. - Cham :Springer Nature Switzerland :2024. - xiii, 140 p. :ill. (chiefly color), digital ;24 cm. - Understanding complex systems,1860-0840. - Understanding complex systems..

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.

ISBN: 9783031731648

Standard No.: 10.1007/978-3-031-73164-8doiSubjects--Topical Terms:

898203
System analysis
--Statistical methods.

LC Class. No.: QA402

Dewey Class. No.: 003

Statistical laws in complex systems = combining mechanistic models and data analysis /
LDR:02566nam a2200337 a 4500 001 1153803
003 DE-He213
005 20241215115226.0
006 m d
007 cr nn 008maaau
008 250619s2024 sz s 0 eng d
020 $a 9783031731648 $q (electronic bk.)
020 $a 9783031731631 $q (paper)
024 7 $a 10.1007/978-3-031-73164-8 $2 doi
035 $a 978-3-031-73164-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402
072 7 $a UN $2 bicssc
072 7 $a COM018000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 003 $2 23
090 $a QA402 $b .A468 2024
100 1 $a Altmann, Eduardo G. $3 1109724
245 1 0 $a Statistical laws in complex systems $h [electronic resource] : $b combining mechanistic models and data analysis / $c by Eduardo G. Altmann.
260 $a Cham : $c 2024. $b Springer Nature Switzerland : $b Imprint: Springer,
300 $a xiii, 140 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Understanding complex systems, $x 1860-0840
505 0 $a Introduction -- Examples of statistical laws -- From data to laws -- Synthesis statistical laws in context -- Datasets and Codes.
520 $a 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.
650 0 $a System analysis $x Statistical methods. $3 898203
650 1 4 $a Data Engineering. $3 1226308
650 2 4 $a Applied Dynamical Systems. $3 1366186
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Understanding complex systems. $3 881607
856 4 0 $u https://doi.org/10.1007/978-3-031-73164-8
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)