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Logarithmic Combinatorial Structures: A Probabilistic Approach
Richard Arratia, University of Southern California, University Park, Los Angeles, CA, A. D. Barbour, University of Zürich, Zurich, Switzerland, and Simon Tavaré, University of Southern California, University Park, Los Angeles, CA
A publication of the European Mathematical Society.
EMS Monographs in Mathematics
2003; 352 pp; hardcover
Volume: 1
ISBN-10: 3-03719-000-0
ISBN-13: 978-3-03719-000-5
List Price: US$79
Member Price: US$63.20
Order Code: EMSMONO/1
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The elements of many classical combinatorial structures can be naturally decomposed into components. Permutations can be decomposed into cycles, polynomials over a finite field into irreducible factors, mappings into connected components. In all of these examples, and in many more, there are strong similarities between the numbers of components of different sizes that are found in the decompositions of "typical" elements of large size. For instance, the total number of components grows logarithmically with the size of the element, and the size of the largest component is an appreciable fraction of the whole.

This book explains the similarities in asymptotic behavior as the result of two basic properties shared by the structures: the conditioning relation and the logarithmic condition. The discussion is conducted in the language of probability, enabling the theory to be developed under rather general and explicit conditions; for the finer conclusions, Stein's method emerges as the key ingredient. The book is thus of particular interest to graduate students and researchers in both combinatorics and probability theory.

A publication of the European Mathematical Society (EMS). Distributed within the Americas by the American Mathematical Society.


Graduate students and research mathematicians interested in probability theory and stochastic processes.

Table of Contents

  • Permutations and primes
  • Decomposable combinatorial structures
  • Probabilistic preliminaries
  • The Ewens sampling formula: methods
  • The Ewens sampling formula: asymptotics
  • Logarithmic combinatorial structures
  • General setting
  • Consequences
  • A Stein equation
  • Point probabilities
  • Distributional comparisons with \(P_\theta\)
  • Comparisons with \(P_\theta\): point probabilities
  • Proofs
  • Technical complements
  • References
  • Notation index
  • Author index
  • Subject index
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