Stochastic Processes with Applications
This book develops systematically and rigorously, yet in an expository and lively manner, the evolution of general random processes and their large time properties such as transience, recurrence, and convergence to steady states. The emphasis is on the most important classes of these processes from the viewpoint of theory as well as applications, namely, Markov processes. The book features very broad coverage of the most applicable aspects of stochastic processes, including sufficient material for self-contained courses on random walks in one and multiple dimensions; Markov chains in discrete and continuous times, including birth-death processes; Brownian motion and diffusions; stochastic optimization; and stochastic differential equations. This book is for graduate students in mathematics, statistics, science and engineering, and it may also be used as a reference by professionals in diverse fields whose work involves the application of probability.
- Numerous extensively worked examples illustrate important applications of the subject
- Some very technical matters are relegated to a Theoretical Complements section at the end of each chapter in order not to impede the flow of the material
- The essentials of measure theoretic probability are included in an appendix to complete some of the more technical aspects of the text
Reviews & endorsements
'This may be the best all-around treatment [of stochastic processes] for use by graduate students with varied backgrounds but with some mathematical ambitions.' William G. Faris, University of Arizona
'The book is remarkably comprehensive. The additional notes at the end of the chapters contain a fund of information.' Richard F. Gundy, Rutgers University
Product details
No date availablePaperback
9780898716894
184 pages
227 × 151 × 32 mm
0.94kg
Table of Contents
- Preface to the Classics Edition
- Preface
- Sample course outline
- 1. Random walk and Brownian motion
- 2, Discrete-parameter Markov chains
- 3. Birth–death Markov chains
- 4. Continuous-parameter Markov chains
- 5. Brownian motion and diffusions
- 6. Dynamic programming and stochastic optimization
- 7. An introduction to stochastic differential equations
- 8. A probability and measure theory overview
- Author index
- Subject index
- Errata.