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  • © 2018

Understanding Markov Chains

Examples and Applications

Authors:

  • Easily accessible to both mathematics and non-mathematics majors who are taking an introductory course on Stochastic Processes
  • Filled with numerous exercises to test students' understanding of key concepts
  • A gentle introduction to help students ease into later chapters, also suitable for self-study
  • Accompanied with computer simulation codes in R and Python
  • Request lecturer material: sn.pub/lecturer-material

Part of the book series: Springer Undergraduate Mathematics Series (SUMS)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xvii
  2. Probability Background

    • Nicolas Privault
    Pages 1-37
  3. Gambling Problems

    • Nicolas Privault
    Pages 39-67
  4. Random Walks

    • Nicolas Privault
    Pages 69-87
  5. Discrete-Time Markov Chains

    • Nicolas Privault
    Pages 89-113
  6. First Step Analysis

    • Nicolas Privault
    Pages 115-145
  7. Classification of States

    • Nicolas Privault
    Pages 147-162
  8. Long-Run Behavior of Markov Chains

    • Nicolas Privault
    Pages 163-188
  9. Branching Processes

    • Nicolas Privault
    Pages 189-209
  10. Continuous-Time Markov Chains

    • Nicolas Privault
    Pages 211-262
  11. Discrete-Time Martingales

    • Nicolas Privault
    Pages 263-280
  12. Spatial Poisson Processes

    • Nicolas Privault
    Pages 281-288
  13. Reliability Theory

    • Nicolas Privault
    Pages 289-293
  14. Back Matter

    Pages 295-372

About this book

This book provides an undergraduate-level introduction to discrete and continuous-time Markov chains and their applications, with a particular focus on the first step analysis technique and its applications to average hitting times and ruin probabilities. It also discusses classical topics such as recurrence and transience, stationary and limiting distributions, as well as branching processes. It first examines in detail two important examples (gambling processes and random walks) before presenting the general theory itself in the subsequent chapters. It also provides an introduction to discrete-time martingales and their relation to ruin probabilities and mean exit times, together with a chapter on spatial Poisson processes. The concepts presented are illustrated by examples, 138 exercises and 9 problems with their solutions.

Authors and Affiliations

  • School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore

    Nicolas Privault

About the author

The author is an associate professor from the Nanyang Technological University (NTU) and is well-established in the field of stochastic processes and a highly respected probabilist. He has authored the book, Stochastic Analysis in Discrete and Continuous Settings: With Normal Martingales, Lecture Notes in Mathematics, Springer, 2009 and was a co-editor for the book, Stochastic Analysis with Financial Applications, Progress in Probability, Vol. 65, Springer Basel, 2011. Aside from these two Springer titles, he has authored several others. He is currently teaching the course M27004-Probability Theory and Stochastic Processes at NTU. The manuscript has been developed over the years from his courses on Stochastic Processes.

Bibliographic Information

Buy it now

Buying options

eBook USD 34.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 44.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access