Introduction to Hidden Semi-Markov Models
Published on 8. February 2018
Book
Paperback/Softback
184 pages
978-1-108-44198-8 (ISBN)
Description
Markov chains and hidden Markov chains have applications in many areas of engineering and genomics. This book provides a basic introduction to the subject by first developing the theory of Markov processes in an elementary discrete time, finite state framework suitable for senior undergraduates and graduates. The authors then introduce semi-Markov chains and hidden semi-Markov chains, before developing related estimation and filtering results. Genomics applications are modelled by discrete observations of these hidden semi-Markov chains. This book contains new results and previously unpublished material not available elsewhere. The approach is rigorous and focused on applications.
Reviews / Votes
'... this book is of interest to researchers attracted by hidden Markov and semi-Markov models. It covers probabilistic and statistical treatments of the considered topics, and introduces the reader ... to possible applications, mainly in genomics. Hence, Ph.D. students and specialists in the area of hidden Markov processes are invited to consider this book as a reference in their activities.' Antonio Di Crescenzo, MathSciNet '... dedicated mostly to graduate students and providing a rigorous and rather complete mathematical introduction to the theory of hidden Markov models as well as hidden semi-Markov models under main assumption that the hidden process is a finite state Markov chain. The semi-Markov models appear when the assumption that the length of time the chain spends in any state is geometrically distributed is relaxed. The authors carefully construct these processes on the canonical probability space and then derive filters and smoother, as well as the Viterbi estimates. The central role plays the EM Algorithm.' Jerzy Ombach, ZB Math ReviewsMore details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Product notice
Paperback (trade)
Illustrations
Worked examples or Exercises
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 10 mm
Weight
279 gr
ISBN-13
978-1-108-44198-8 (9781108441988)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
John van der Hoek | Robert J. Elliott
Introduction to Hidden Semi-Markov Models
E-Book
03/2018
Cambridge University Press
€58.99
Available for download
John van der Hoek
Introduction to Hidden Semi-Markov Models
E-Book
02/2018
Cambridge University Press
€48.99
Available for download
John van der Hoek | Robert J. Elliott
Introduction to Hidden Semi-Markov Models
Book
Cambridge University Press
Unfortunately, price unknown
The article will not be published
Persons
John van der Hoek is an Associate Professor at the University of South Australia. He has authored papers in partial differential equations, free boundary value problems, numerical analysis, stochastic analysis, actuarial science and mathematical finance. With Robert Elliott he co-authored Binomial Methods in Finance. Robert J. Elliott is a Research Professor at the University of South Australia. Previously he held positions at universities around the world, including Yale, Oxford, Alberta, Calgary and Adelaide. He has authored nine books, including Mathematics of Financial Markets (2004, with P. E. Kopp) and Stochastic Calculus and Application (1982).
Content
Preface; 1. Observed Markov chains; 2. Estimation of an observed Markov chain; 3. Hidden Markov models; 4. Filters and smoothers; 5. The Viterbi algorithm; 6. The EM algorithm; 7. A new Markov chain model; 8. Semi-Markov models; 9. Hidden semi-Markov models; 10. Filters for hidden semi-Markov models; Appendix A. Higher order chains; Appendix B. An example of a second order chain; Appendix C. A conditional Bayes theorem; Appendix D. On conditional expectations; Appendix E. Some molecular biology; Appendix F. Earlier applications of hidden Markov chain models; References; Index.