An Introduction to Stochastic Processes and Their Applications (Springer Series in Statistics)

Todorovic, Petar

ISBN 10: 1461397448 / ISBN 13: 9781461397441

Published by Springer, 2011

About this Item

LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS.

Seller Inventory # ERICA79614613974486

Bibliographic Details

• Title: An Introduction to Stochastic Processes and Their Applications (Springer Series in Statistics)
• Publisher: Springer
• Publication Date: 2011
• Language: English
• ISBN 10: 1461397448
• ISBN 13: 9781461397441
• Binding: Paperback
• Condition: Like New
• Book Type: book

About this title

Synopsis

This text on stochastic processes and their applications is based on a set of lectures given during the past several years at the University of California, Santa Barbara (UCSB). It is an introductory graduate course designed for classroom purposes. Its objective is to provide graduate students of statistics with an overview of some basic methods and techniques in the theory of stochastic processes. The only prerequisites are some rudiments of measure and integration theory and an intermediate course in probability theory. There are more than 50 examples and applications and 243 problems and complements which appear at the end of each chapter. The book consists of 10 chapters. Basic concepts and definitions are pro­ vided in Chapter 1. This chapter also contains a number of motivating ex­ amples and applications illustrating the practical use of the concepts. The last five sections are devoted to topics such as separability, continuity, and measurability of random processes, which are discussed in some detail. The concept of a simple point process on R+ is introduced in Chapter 2. Using the coupling inequality and Le Cam's lemma, it is shown that if its counting function is stochastically continuous and has independent increments, the point process is Poisson. When the counting function is Markovian, the sequence of arrival times is also a Markov process. Some related topics such as independent thinning and marked point processes are also discussed. In the final section, an application of these results to flood modeling is presented.