Greg Leibon, Scott Pauls, Dan Rockmore

Data Analysis for Complex Systems

A Linear Algebra Approach

• Broschiertes Buch

Produktbeschreibung

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

Produktdetails

• Verlag: Princeton University Press
• Seitenzahl: 168
• Erscheinungstermin: 25. Dezember 2040
• Englisch
• ISBN-13: 9780691139180
• ISBN-10: 0691139180
• Artikelnr.: 58120179

Autorenporträt

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.