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Introduction to Hidden Semi-Markov Models Vital Source e-bog
John van der Hoek
(2018)
Cambridge University Press
724,00 kr.
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Introduction to Hidden Semi-Markov Models
John van der Hoek og Robert J. Elliott
(2018)
Sprog: Engelsk
Cambridge University Press
737,00 kr.
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Detaljer om varen
- Vital Source searchable e-book (Fixed pages)
- Udgiver: Cambridge University Press (Februar 2018)
- ISBN: 9781108383905
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 This book contains new results and previously unpublished material not available elsewhere.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.
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Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: -1 sider kan printes ad gangen
Copy: højest -1 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Paperback: 184 sider
- Udgiver: Cambridge University Press (Februar 2018)
- Forfattere: John van der Hoek og Robert J. Elliott
- ISBN: 9781108441988
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 This book contains new results and previously unpublished material not available elsewhere.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.
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.
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.
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