Stochastic processes

Emanuel Parzen9780898714418, 0898714419

This introductory textbook explains how and why probability models are applied to scientific fields such as medicine, biology, physics, oceanography, economics, and psychology to solve problems about stochastic processes. It does not just show how a problem is solved but explains why by formulating questions and first steps in the solutions.
Stochastic Processes is ideal for a course aiming to give examples of the wide variety of empirical phenomena for which stochastic processes provide mathematical models. It introduces the methods of probability model building and provides the reader with mathematically sound techniques as well as the ability to further study the theory of stochastic processes.
Originally published in 1962, this was the first comprehensive survey of stochastic processes requiring only a minimal background in introductory probability theory and mathematical analysis. Stochastic Processes continues to be unique, with many topics and examples still not discussed in other textbooks. As new fields of applications (such as finance and DNA analysis) become important, researchers will continue to find the fundamental and accessible topics explained in this book essential background for their research.

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Table of contents :
Stochastic Processes……Page 1
Table of contents……Page 10
Preface to the Classics Edition……Page 14
Preface……Page 18
Role of the theory of stochastic processes……Page 22
CHAPTER 1 Random variables and stochastic processes……Page 28
CHAPTER 2 Conditional probability and conditional expectation……Page 62
CHAPTER 3 Normal processes and covariance stationary processes……Page 87
CHAPTER 4 Counting processes and Poisson processes……Page 138
CHAPTER 5 Renewal counting processes……Page 181
CHAPTER 6 Markov chains:discrete parameter……Page 208
CHAPTER 7 Markov chains:continuous parameter……Page 297
References……Page 328
Author index……Page 335
Subject index……Page 337