Applied Probability and Stochastic Processes

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A01=Frank Beichelt
Author_Frank Beichelt
Autoregressive Sequence
Branching processes
Brownian Motion
Brownian Motion Process
Category=PBT
Conditional Probability Distribution
Continuous Time Martingales
Continuous Time Stochastic Process
Covariance Function
Discrete Random Variables
Discrete Time Markov Chain
Discrete Time Stochastic Process
Discrete White Noise
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Exponential Martingale
Geometric Brownian Motion
Geometric Random Walk
Joint Distribution Function
Joint Survival Probability
Local Limit Theorem
Maintenance Cost Rate
Markov models
Marshall Olkin Distribution
Operations research
Option pricing
Ornstein Uhlenbeck Process
probability modeling
Probability theory
Queueing models
Random Variables
Random Vector
Risk analysis
risk assessment
Sample Path
Spectral analysis
spectral methods
stationary processes
Stochastic modeling
stochastic modeling applications
Stochastic Process
time series analysis
Trend Function

Product details

  • ISBN 9780367658496
  • Weight: 880g
  • Dimensions: 156 x 234mm
  • Publication Date: 30 Sep 2020
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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.

Frank Beichelt is an honorary professor in the School of Statistics and Actuarial Science at the University of Witwatersrand. His research focuses on probability theory and mathematical statistics, including stochastic modeling in reliability, maintenance, and safety analysis. He is the author/coauthor of numerous papers and books, including the Chapman & Hall/CRC book Reliability and Maintenance: Networks and Systems. He holds a Dr. rer. nat. in mathematics and a Dr. sc. in engineering.

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