Hidden Markov Processes: Theory and Applications to Biology
This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. The book starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematica...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Princeton
Princeton University Press
2014
|
| Schriftenreihe: | Princeton Series in Applied Mathematics
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| Schlagworte: | |
| Zusammenfassung: | This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. The book starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron-Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum-Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. The book also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 online resource (343 pages) |
| ISBN: | 9781400850518 9780691133157 |
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Datensatz im Suchindex
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| author | Vidyasagar, M. |
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| dewey-full | 519.233 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
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| dewey-sort | 3519.233 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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| spelling | Vidyasagar, M. Verfasser aut Hidden Markov Processes Theory and Applications to Biology Princeton Princeton University Press 2014 © 2014 1 online resource (343 pages) txt rdacontent c rdamedia cr rdacarrier Princeton Series in Applied Mathematics Description based on publisher supplied metadata and other sources This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. The book starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron-Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum-Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. The book also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored Computational biology Markov processes Hidden-Markov-Modell (DE-588)4352479-5 gnd rswk-swf Markov-Prozess (DE-588)4134948-9 gnd rswk-swf Markov-Prozess (DE-588)4134948-9 s Hidden-Markov-Modell (DE-588)4352479-5 s 1\p DE-604 Erscheint auch als Druck-Ausgabe Vidyasagar, M . Hidden Markov Processes : Theory and Applications to Biology 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Vidyasagar, M. Hidden Markov Processes Theory and Applications to Biology Computational biology Markov processes Hidden-Markov-Modell (DE-588)4352479-5 gnd Markov-Prozess (DE-588)4134948-9 gnd |
| subject_GND | (DE-588)4352479-5 (DE-588)4134948-9 |
| title | Hidden Markov Processes Theory and Applications to Biology |
| title_auth | Hidden Markov Processes Theory and Applications to Biology |
| title_exact_search | Hidden Markov Processes Theory and Applications to Biology |
| title_full | Hidden Markov Processes Theory and Applications to Biology |
| title_fullStr | Hidden Markov Processes Theory and Applications to Biology |
| title_full_unstemmed | Hidden Markov Processes Theory and Applications to Biology |
| title_short | Hidden Markov Processes |
| title_sort | hidden markov processes theory and applications to biology |
| title_sub | Theory and Applications to Biology |
| topic | Computational biology Markov processes Hidden-Markov-Modell (DE-588)4352479-5 gnd Markov-Prozess (DE-588)4134948-9 gnd |
| topic_facet | Computational biology Markov processes Hidden-Markov-Modell Markov-Prozess |
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