Introduction to stochastic processes (Record no. 72637)

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fixed length control field 03706nam a2200421 a 4500
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control field 0009903
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210616s2021 si ob 001 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9789814740319
-- (ebook)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9814740314
-- (ebook)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- (hbk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- (hbk.)
082 00 - CLASSIFICATION NUMBER
Call Number 519.2/3
100 1# - AUTHOR NAME
Author Chen, Mu-Fa.
245 10 - TITLE STATEMENT
Title Introduction to stochastic processes
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication Singapore :
Publisher World Scientific,
Year of publication 2021.
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 online resource (244 p.).
490 1# - SERIES STATEMENT
Series statement World Scientific series on probability theory and its applications ;
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Preface to the English edition -- Preface to the Chinese edition -- Markov processes. Discrete-time Markov chains -- Continuous-time Markov chains -- Reversible Markov chains -- General Markov processes -- Stochastic analysis. Martingale --Brownian motion -- stochastic integral and diffusion processes -- Semimartingale and stochastic integral - Notes - Bibliography - Index.
520 ## - SUMMARY, ETC.
Summary, etc "The objective of this book is to introduce the elements of stochastic processes in a rather concise manner where we present the two most important parts -- Markov chains and stochastic analysis. The readers are led directly to the core of the main topics to be treated in the context. Further details and additional materials are left to a section containing abundant exercises for further reading and studying. In the part on Markov chains, the focus is on the ergodicity. By using the minimal nonnegative solution method, we deal with the recurrence and various types of ergodicity. This is done step by step, from finite state spaces to denumerable state spaces, and from discrete time to continuous time. The methods of proofs adopt modern techniques, such as coupling and duality methods. Some very new results are included, such as the estimate of the spectral gap. The structure and proofs in the first part are rather different from other existing textbooks on Markov chains. In the part on stochastic analysis, we cover the martingale theory and Brownian motions, the stochastic integral and stochastic differential equations with emphasis on one dimension, and the multidimensional stochastic integral and stochastic equation based on semimartingales. We introduce three important topics here: the Feynman-Kac formula, random time transform and Girsanov transform. As an essential application of the probability theory in classical mathematics, we also deal with the famous Brunn-Minkowski inequality in convex geometry. This book also features modern probability theory that is used in different fields, such as MCMC, or even deterministic areas: convex geometry and number theory. It provides a new and direct routine for students going through the classical Markov chains to the modern stochastic analysis"--Publisher's website.
700 1# - AUTHOR 2
Author 2 Mao, Yong-Hua.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://www.worldscientific.com/worldscibooks/10.1142/9903#t=toc
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Stochastic processes.

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