Stochastic Processes: Theory for Applications

Editorial Reviews

This definitive textbook provides a solid introduction to discrete and continuous stochastic processes, tackling a complex field in a way that instils a deep understanding of the relevant , and develops an intuitive grasp of the way these can be applied to modelling real-world . It includes a careful review of elementary probability and detailed coverage of Poisson, Gaussian and Markov processes with richly varied queuing applications. The and applications of inference, hypothesis , estimation, random walks, large deviations, martingales and investments are developed. Written by one of the world’s leading theorists, evolving over twenty years of graduate classroom teaching and enriched by over 300 exercises, this is an exceptional resource for anyone looking to develop their understanding of stochastic processes.

Table of Contents

Chapter 1 Introduction and review of probability
Chapter 2 Poisson processes
Chapter 3 Gaussian random vectors and processes
Chapter 4 Finite-state Markov chains
Chapter 5 Renewal processes
Chapter 6 Countable-state Markov chains
Chapter 7 Markov processes with countable-state spaces
Chapter 8 Detection, decisions, and hypothesis testing
Chapter 9 Random walks, large deviations, and martingales
Chapter 10 Estimation

Book Details

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  • Title: Stochastic Processes: Theory for Applications
  • Author:
  • Length: 553 pages
  • Edition: 1
  • Language: English
  • Publisher:
  • Publication Date: 2014-01-31
  • ISBN-10: 1107039754
  • ISBN-13: 9781107039759
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