Selfsimilar Processes by Paul Embrechts
The modeling of stochastic dependence is fundamental for understanding random systems evolving in time. With an historical overview, this book describes the state of knowledge about selfsimilar processes and their applications. It emphasizes concepts, definitions and basic properties, giving the reader a road map of the realm of selfsimilarity.-
Analytic Theory of Global Bifurcation
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Thermodynamics
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Stability and Control of Large-Scale Dynamical Systems
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Entropy
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Totally Nonnegative Matrices
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Distributed Control of Robotic Networks
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Positive Definite Matrices
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Optimization
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Optimization and Learning via Stochastic Gradient Search
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Rays, Waves, and Scattering
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The Traveling Salesman Problem
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Statistical Inference via Convex Optimization
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Graph Theoretic Methods in Multiagent Networks
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Algebraic Curves over a Finite Field
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A Dynamical Systems Theory of Thermodynamics
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Formal Verification of Control System Software
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Robust Optimization
"Authoritative and written by leading experts, this book is a significant contribution to a growing fieldSelfsimilar processes crop up in a wide range of subjects from finance to physics, so this book will have a correspondingly wide readership."—Chris Rogers, Bath University
"This is a timely book. Everybody is talking about scaling, and selfsimilar stochastic processes are the basic and the clearest examples of models with scaling. In applications from finance to communication networks, selfsimilar processes are believed to be important. Yet much of what is known about them is folklore; this book fills the void and gives reader access to some hard facts. And because this book requires only modest mathematical sophistication, it is accessible to a wide audience."—Gennady Samorodnitsky, Cornell University
"This is a timely book. Everybody is talking about scaling, and selfsimilar stochastic processes are the basic and the clearest examples of models with scaling. In applications from finance to communication networks, selfsimilar processes are believed to be important. Yet much of what is known about them is folklore; this book fills the void and gives reader access to some hard facts. And because this book requires only modest mathematical sophistication, it is accessible to a wide audience."—Gennady Samorodnitsky, Cornell University
Paul Embrechts is Professor of Mathematics at the Swiss Federal Institute of Technology (ETHZ), Zurich, Switzerland. He is the author of numerous scientific papers on stochastic processes and their applications and the coauthor of the influential book on "Modelling of Extremal Events for Insurance and Finance". Makoto Maejima is Professor of Mathematics at Keio University, Yokohama, Japan. He has published extensively on selfsimilarity and stable processes.
| SKU | Unavailable |
| ISBN 13 | 9780691096278 |
| ISBN 10 | 0691096279 |
| Title | Selfsimilar Processes |
| Author | Paul Embrechts |
| Series | Princeton Series In Applied Mathematics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Princeton University Press |
| Year published | 2002-08-05 |
| Number of pages | 128 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |