Introduction to Hidden Semi-Markov Models: London Mathematical Society Lecture Note Series, cartea 445
Autor John van der Hoek, Robert J. Elliotten Limba Engleză Paperback – 7 feb 2018
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Specificații
ISBN-13: 9781108441988
ISBN-10: 110844198X
Pagini: 184
Dimensiuni: 151 x 227 x 11 mm
Greutate: 0.29 kg
Editura: Cambridge University Press
Colecția Cambridge University Press
Seria London Mathematical Society Lecture Note Series
Locul publicării:Cambridge, United Kingdom
ISBN-10: 110844198X
Pagini: 184
Dimensiuni: 151 x 227 x 11 mm
Greutate: 0.29 kg
Editura: Cambridge University Press
Colecția Cambridge University Press
Seria London Mathematical Society Lecture Note Series
Locul publicării:Cambridge, United Kingdom
Cuprins
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.
Recenzii
'… 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 Reviews
'… 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 Reviews
Notă biografică
Descriere
Develops the theory of Markov and semi-Markov processes in an elementary setting suitable for senior undergraduate and graduate students.