ABSTRACT

Applied Probability and Stochastic Processes, Second Edition presents a self-contained introduction to elementary probability theory and stochastic processes with a special emphasis on their applications in science, engineering, finance, computer science, and operations research. It covers the theoretical foundations for modeling time-dependent random phenomena in these areas and illustrates applications through the analysis of numerous practical examples. The author draws on his 50 years of experience in the field to give your students a better understanding of probability theory and stochastic processes and enable them to use stochastic modeling in their work.

New to the Second Edition

  • Completely rewritten part on probability theory—now more than double in size
  • New sections on time series analysis, random walks, branching processes, and spectral analysis of stationary stochastic processes
  • Comprehensive numerical discussions of examples, which replace the more theoretically challenging sections
  • Additional examples, exercises, and figures

Presenting the material in a student-friendly, application-oriented manner, this non-measure theoretic text only assumes a mathematical maturity that applied science students acquire during their undergraduate studies in mathematics. Many exercises allow students to assess their understanding of the topics. In addition, the book occasionally describes connections between probabilistic concepts and corresponding statistical approaches to facilitate comprehension. Some important proofs and challenging examples and exercises are also included for more theoretically interested readers.

chapter |6 pages

Introduction

part I|214 pages

Probability Theory

chapter 1|32 pages

Random Events and Their Probabilities

chapter 2|78 pages

One-Dimensional Random Variables

chapter 3|38 pages

Multidimensional Random Variables

chapter 4|44 pages

Functions of Random Variables

chapter 5|22 pages

Inequalities and Limit Theorems

part II|34 pages

Stochastic Processes

chapter 6|34 pages

Basics of Stochastic Processes

chapter 7|84 pages

Random Point Processes

chapter 8|44 pages

Discrete-Time Markov Chains

chapter 9|92 pages

Continuous-Time Markov Chains

chapter 10|20 pages

Martingales

chapter 11|36 pages

Brownian Motion

chapter 12|18 pages

Spectral Analysis of Stationary Processes