Stochastic Processes in Science, Engineering, and Finance

Stochastic Processes in Science, Engineering, and Finance

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This book presents a self-contained introduction to stochastic processes with emphasis on their applications in science, engineering, finance, computer science, and operations research. It provides theoretical foundations for modeling time-dependent random phenomena in these areas and illustrates their application by analyzing numerous practical examples. The treatment assumes few prerequisites, requiring only the standard mathematical maturity acquired by undergraduate applied science students. It includes an introductory chapter that summarizes the basic probability theory needed as background. Numerous exercises reinforce the concepts and techniques discussed and allow readers to assess their grasp of the subject. Solutions to most of the exercises are provided in an appendix. While focused primarily on practical aspects, the presentation includes some important proofs along with more challenging examples and exercises for those more theoretically inclined. Mastering the contents of this book prepares readers to apply stochastic modeling in their own fields and enables them to work more creatively with software designed for dealing with the data analysis aspects of stochastic more

Product details

  • Hardback | 440 pages
  • 152.4 x 233.7 x 27.9mm | 703.08g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 56 black & white illustrations, 2 black & white tables
  • 1584884932
  • 9781584884934
  • 2,405,959

Review quote

"Beichelt offers a well-written, entertaining work on stochastic processes that emphasizes practical applications. [He] offers a rare opportunity to understand the practical implications of the theory involved. Though the material covered in each chapter can be found elsewhere, the tandem of practical focus and theoretical clarity leads to a product that makes the book indispensable." M. Bona, University of Florida, CHOICE, Vol. 44, No. 5, January 2007show more

Table of contents

Probability Theory Basics of Stochastic Processes Random Point Processes Markov Chains in Discrete Time Markov Chains in Continuous Time Martingales Brownian Motionshow more

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