Renewal theory for perturbed random walks and similar processes, Iksanov, Alexander
Автор: Hans-G?rg Roos; Martin Stynes; Lutz Tobiska Название: Robust Numerical Methods for Singularly Perturbed Differential Equations ISBN: 3642070825 ISBN-13(EAN): 9783642070822 Издательство: Springer Рейтинг: Цена: 135090.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This new edition incorporates new developments in numerical methods for singularly perturbed differential equations, focusing on linear convection-diffusion equations and on nonlinear flow problems that appear in computational fluid dynamics.
Автор: Dmitrii Silvestrov, Sergei Silvestrov Название: Nonlinearly perturbed semi-Markov processes / ISBN: 3319609874 ISBN-13(EAN): 9783319609874 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The book presents new methods of asymptotic analysis for nonlinearly perturbed semi-Markov processes with a finite phase space.
Автор: Ciprian Tudor Название: Analysis of Variations for Self-similar Processes ISBN: 3319033689 ISBN-13(EAN): 9783319033686 Издательство: Springer Рейтинг: Цена: 79190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents basic properties of self-similar processes, focusing on the study of their variation using stochastic analysis, and also surveys recent techniques and findings on limit theorems and Malliavin calculus.
Автор: Vladas Pipiras; Murad S. Taqqu Название: Stable Non-Gaussian Self-Similar Processes with Stationary Increments ISBN: 3319623303 ISBN-13(EAN): 9783319623306 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides a self-contained presentation on the structure of a large class of stable processes, known as self-similar mixed moving averages. The first sections in the book review random variables, stochastic processes, and integrals, moving on to rigidity and flows, and finally ending with mixed moving averages and self-similarity.
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