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Probability and Stochastics

By Erhan Çınlar

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Probability and Stochastics provides an introduction to the field, and begins by describing the fundamentals and basic principles of probability theory. Later chapters discuss more advanced topics, such as Martingales, Poisson Random Measures, Levy Processes and Brownian Motion.

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  • ISBN13: 978-0-3878-7858-4
  • 572 Pages
  • Publication Date: February 21, 2011
  • Available eBook Formats: PDF
Full Description
This text is an introduction to the modern theory and applications of probability and stochastics. The style and coverage is geared towards the theory of stochastic processes, but with some attention to the applications. In many instances the gist of the problem is introduced in practical, everyday language and then is made precise in mathematical form. The first four chapters are on probability theory: measure and integration, probability spaces, conditional expectations, and the classical limit theorems. There follows chapters on martingales, Poisson random measures, Levy Processes, Brownian motion, and Markov Processes.Special attention is paid to Poisson random measures and their roles in regulating the excursions of Brownian motion and the jumps of Levy and Markov processes. Each chapter has a large number of varied examples and exercises.   The book is based on the author’s lecture notes in courses offered over the years at Princeton University. These courses attracted graduate students from engineering, economics, physics, computer sciences, and mathematics. Erhan Cinlar has received many awards for excellence in teaching, including the President’s Award for Distinguished Teaching at Princeton University. His research interests include theories of Markov processes, point processes, stochastic calculus, and stochastic flows. The book is full of insights and observations that only a lifetime researcher in probability can have, all told in a lucid yet precise style.
Table of Contents

Table of Contents

  1. Preface.
  2. Measure and Integration.
  3. Probability Spaces.
  4. Convergence.
  5. Conditioning.
  6. Martingales and Stochastics.
  7. Poisson Random Measures.
  8. Levy Processes.
  9. Index.
  10. Bibliography

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