Includes bibliographical references (p. 442-458) and index.
|Series||Applied mathematical sciences -- v. 170, Applied mathematical sciences (Springer-Verlag New York Inc.) -- v. 170|
|LC Classifications||QC20.7.S8 S38 2010|
|The Physical Object|
|Pagination||xvii, 468 p. :|
|Number of Pages||468|
|ISBN 10||1441916040, 1441916059|
|ISBN 10||9781441916044, 9781441916051|
|LC Control Number||2009942425|
Theory and Statistical Applications of Stochastic Processes. Author (s): Yuliya Mishura. Georgiy Shevchenko. First published November Print ISBN |Online ISBN |DOI/ © ISTE Ltd This book is a collection of exercises covering all the main topics in the modern theory of stochastic processes and its applications, including finance, actuarial mathematics, queuing theory, and risk theory. The aim of this book is to provide the reader with the theoretical and practical material necessary for deeper understanding of the main topics in the theory of stochastic processes and its related by: 5. Description. This book is concerned with the theory of stochastic processes and the theoretical aspects of statistics for stochastic processes. It combines classic topics such as construction of stochastic processes, associated filtrations, processes with independent increments, Gaussian processes, martingales, Markov properties, continuity and related properties of trajectories with. Introduction This book presents various results and techniques from the theory of stochastic processes that are useful in the study of stochastic problems in the natural sciences. The main focus is analytical methods, although numerical methods and statistical inference methodologies for studying diffusion processes are also presented.
This book is a collection of exercises covering all the main topics in the modern theory of stochastic processes and its applications, including finance, actuarial mathematics, queuing theory, and risk theory. The aim of this book is to provide the reader with the theoretical and practical material necessary for deeper understanding of the main. The theory and applications of inference, hypothesis testing, estimation, random walks, large deviations, martingales and investments are developed. Written by one of the world's leading information theorists, evolving over twenty years of graduate classroom teaching and enriched by over exercises, this is an exceptional resource for anyone. The book is an introduction to stochastic processes with applications from physics and finance. It introduces the basic notions of probability theory and the mathematics of stochastic processes. The applications that we discuss are chosen to show the interdisciplinary character of the concepts and methods and are taken from physics. Request PDF | On Jan 1, , Zeev Schuss published Theory and Applications of Stochastic Processes: An Analytical Approach | Find, read and cite all the research you need on ResearchGate.
Stochastic Processes and their Applications publishes papers on the theory and applications of stochastic processes. It is concerned with concepts and techniques, and is oriented towards a broad spectrum of mathematical, scientific and engineering interests. . This definitive textbook provides a solid introduction to stochastic processes, covering both theory and applications. It is written by one of the world's leading information theorists, evolving over twenty years of graduate classroom teaching, and is accompanied by over exercises, with online solutions for by: Stochastic Processes Theory and Applications. Alexander Zeifman, Victor Korolev and Alexander Sipin (Eds.) Pages: Published: December (This book is a printed edition of the Special Issue Stochastic Processes: Theory and Applications that was published in Mathematics) Download PDF. This concisely written book is a rigorous and self-contained introduction to the theory of continuous-time stochastic processes. A balance of theory and applications, the work features concrete examples of modeling real-world problems from biology, medicine, industrial applications, finance, and insurance using stochastic methods.