Understanding Markov Chains: Examples and Applications

Privault, Nicolas

ISBN 10: 9811306583 / ISBN 13: 9789811306587

Published by Springer Verlag, 2018

About this Item

2nd edition. 372 pages. 9.00x6.00x0.75 inches. In Stock.

Seller Inventory # x-9811306583

Bibliographic Details

• Title: Understanding Markov Chains: Examples and Applications
• Publisher: Springer Verlag
• Publication Date: 2018
• Language: English
• ISBN 10: 9811306583
• ISBN 13: 9789811306587
• Binding: Paperback
• Edition: 2nd Edition
• Condition: Brand New

About this title

Synopsis

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.

About the Author

The author is a 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 MH4514 Financial Mathematics at NTU. The manuscript has been developed over the years from his courses on Stochastic Processes.