Large Deviations for Random Graphs: École d'Été de Probabilités de Saint-Flour XLV - 2015 - Softcover

Buch 18 von 20: Lecture Notes in Mathematics

Chatterjee, Sourav

 
9783319658155: Large Deviations for Random Graphs: École d'Été de Probabilités de Saint-Flour XLV - 2015

Inhaltsangabe

First book-length treatment of large deviations for random graphs, plus a chapter on exponential random graphs

Contains a summary of important results from graph limit theory with complete proofs

Written in a style for beginning graduate students, self-contained with essentially no need for background knowledge other than some amount of graduate probability and analysis

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Über die Autorin bzw. den Autor

Sourav Chatterjee is a Professor of Statistics and Mathematics at Stanford University. He has previously taught at the University of California at Berkeley and at the Courant Institute of Mathematical Sciences. He has won several international awards for his work in probability theory, including the Rollo Davidson Prize (2010), the Doeblin Prize (2012) and the Loève Prize (2013) and has received the invitation to speak at the International Congress of Mathematicians in 2014.

Von der hinteren Coverseite

This book addresses the emerging body of literature on the study of rare events in random graphs and networks. For example, what does a random graph look like if by chance it has far more triangles than expected? Until recently, probability theory offered no tools to help answer such questions. Important advances have been made in the last few years, employing tools from the newly developed theory of graph limits. This work represents the first book-length treatment of this area, while also exploring the related area of exponential random graphs. All required results from analysis, combinatorics, graph theory and classical large deviations theory are developed from scratch, making the text self-contained and doing away with the need to look up external references. Further, the book is written in a format and style that are accessible for beginning graduate students in mathematics and statistics.

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