Hidden Markov Processes: Theory and Applications to Biology
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Princeton, N.J.
Princeton University Press
[2014]
|
| Schriftenreihe: | Princeton Series in Applied Mathematics
|
| Schlagworte: | |
| Online-Zugang: | FAB01 FAW01 FCO01 FHA01 FKE01 FLA01 UPA01 Volltext |
| Beschreibung: | 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: | 1 Online-Ressource (312p.) |
| ISBN: | 9781400850518 |
| DOI: | 10.1515/9781400850518 |
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| id | DE-604.BV042523170 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:24:02Z |
| institution | BVB |
| isbn | 9781400850518 |
| language | English |
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| oclc_num | 888550795 |
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| physical | 1 Online-Ressource (312p.) |
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| spelling | Vidyasagar, M. Verfasser aut Hidden Markov Processes Theory and Applications to Biology M. Vidyasagar Princeton, N.J. Princeton University Press [2014] 1 Online-Ressource (312p.) txt rdacontent c rdamedia cr rdacarrier Princeton Series in Applied Mathematics 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 In English Mathematik Computational biology Markov processes Biology Stochastic processes Markov-Prozess (DE-588)4134948-9 gnd rswk-swf Hidden-Markov-Modell (DE-588)4352479-5 gnd rswk-swf Markov-Prozess (DE-588)4134948-9 s Hidden-Markov-Modell (DE-588)4352479-5 s 1\p DE-604 https://doi.org/10.1515/9781400850518 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Vidyasagar, M. Hidden Markov Processes Theory and Applications to Biology Mathematik Computational biology Markov processes Biology Stochastic processes Markov-Prozess (DE-588)4134948-9 gnd Hidden-Markov-Modell (DE-588)4352479-5 gnd |
| subject_GND | (DE-588)4134948-9 (DE-588)4352479-5 |
| 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 M. Vidyasagar |
| title_fullStr | Hidden Markov Processes Theory and Applications to Biology M. Vidyasagar |
| title_full_unstemmed | Hidden Markov Processes Theory and Applications to Biology M. Vidyasagar |
| title_short | Hidden Markov Processes |
| title_sort | hidden markov processes theory and applications to biology |
| title_sub | Theory and Applications to Biology |
| topic | Mathematik Computational biology Markov processes Biology Stochastic processes Markov-Prozess (DE-588)4134948-9 gnd Hidden-Markov-Modell (DE-588)4352479-5 gnd |
| topic_facet | Mathematik Computational biology Markov processes Biology Stochastic processes Markov-Prozess Hidden-Markov-Modell |
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