9780412995118-0412995115-Introduction to Stochastic Processes (Chapman & Hall/CRC Probability Series)

Introduction to Stochastic Processes (Chapman & Hall/CRC Probability Series)

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ISBN-13: 9780412995118
ISBN-10: 0412995115
Edition: 1
Author: Gregory F. Lawler
Publication date: 1995
Publisher: Chapman and Hall/CRC
Format: Hardcover 192 pages
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Book details

ISBN-13: 9780412995118
ISBN-10: 0412995115
Edition: 1
Author: Gregory F. Lawler
Publication date: 1995
Publisher: Chapman and Hall/CRC
Format: Hardcover 192 pages

Summary

Introduction to Stochastic Processes (Chapman & Hall/CRC Probability Series) (ISBN-13: 9780412995118 and ISBN-10: 0412995115), written by authors Gregory F. Lawler, was published by Chapman and Hall/CRC in 1995. With an overall rating of 3.8 stars, it's a notable title among other Applied (Mathematics) books. You can easily purchase or rent Introduction to Stochastic Processes (Chapman & Hall/CRC Probability Series) (Hardcover, Used) from BooksRun, along with many other new and used Applied books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.01.

Description

This concise, informal introduction to stochastic processes evolving with time was designed to meet the needs of graduate students not only in mathematics and statistics, but in the many fields in which the concepts presented are important, including computer science, economics, business, biological science, psychology, and engineering.

With emphasis on fundamental mathematical ideas rather than proofs or detailed applications, the treatment introduces the following topics:

  • Markov chains, with focus on the relationship between the convergence to equilibrium and the size of the eigenvalues of the stochastic matrix
  • Infinite state space, including the ideas of transience, null recurrence and positive recurrence
  • The three main types of continual time Markov chains and optimal stopping of Markov chains
  • Martingales, including conditional expectation, the optional sampling theorem, and the martingale convergence theorem
  • Renewal process and reversible Markov chains
  • Brownian motion, both multidimensional and one-dimensional

    Introduction to Stochastic Processes is ideal for a first course in stochastic processes without measure theory, requiring only a calculus-based undergraduate probability course and a course in linear algebra.
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