Data Analysis for Complex Systems
A Linear Algebra Approach
Seiten
2024
Princeton University Press (Verlag)
978-0-691-13918-0 (ISBN)
Princeton University Press (Verlag)
978-0-691-13918-0 (ISBN)
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The analysis of complex systems-from financial markets and voting patterns to ecosystems and food webs-can be daunting for newcomers to the subject, in part because existing methods often require expertise across multiple disciplines. This book shows how a single technique-the partition decoupling method-can serve as a useful first step for modeling and analyzing complex systems data. Accessible to a broad range of backgrounds and widely applicable to complex systems represented as high-dimensional or network data, this powerful methodology draws on core concepts in network modeling and analysis, cluster analysis, and a range of techniques for dimension reduction. The book explains these and other essential concepts and provides several real-world examples to illustrate how a data-driven approach can illuminate complex systems.
Provides a comprehensive introduction to modeling and analysis of complex systems with minimal mathematical prerequisites
Focuses on a single technique, thereby providing an easy entry point to the subject
Explains analytic techniques using actual data from the social sciences
Uses only linear algebra to model and analyze large data sets
Includes problems and real-world examples
An ideal textbook for students and invaluable resource for researchers with a wide range of backgrounds and preparation
Proven in the classroom
Provides a comprehensive introduction to modeling and analysis of complex systems with minimal mathematical prerequisites
Focuses on a single technique, thereby providing an easy entry point to the subject
Explains analytic techniques using actual data from the social sciences
Uses only linear algebra to model and analyze large data sets
Includes problems and real-world examples
An ideal textbook for students and invaluable resource for researchers with a wide range of backgrounds and preparation
Proven in the classroom
Greg Leibon is chief technology officer and cofounder of Coherent Path, a company specializing in predictive analytics. Scott D. Pauls is professor of mathematics at Dartmouth College. Dan Rockmore is the William H. Neukom 1964 Distinguished Professor of Computational Science at Dartmouth.
| Erscheint lt. Verlag | 19.3.2024 |
|---|---|
| Reihe/Serie | Primers in Complex Systems |
| Zusatzinfo | 35 b/w illus. |
| Verlagsort | New Jersey |
| Sprache | englisch |
| Maße | 140 x 216 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Algebra |
| Mathematik / Informatik ► Mathematik ► Analysis | |
| Naturwissenschaften ► Physik / Astronomie | |
| ISBN-10 | 0-691-13918-0 / 0691139180 |
| ISBN-13 | 978-0-691-13918-0 / 9780691139180 |
| Zustand | Neuware |
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Buch | Softcover (2022)
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