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Hidden Markov Models for Bioinformatics - Koski, T.
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Koski, T.:

Hidden Markov Models for Bioinformatics - new book

2001, ISBN: 9781402001369

Springer-Verlag New York Inc. Broché, Auflage: Softcover reprint of the original 1st ed. 2001, 391 Seiten, Publiziert: 2001-11-30T00:00:01Z, Produktgruppe: Livre, 2.82 kg, Livres anglais … More...

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Hidden Markov Models for Bioinformatics (Computational Biology, 2, Band 2) - T.
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T.:

Hidden Markov Models for Bioinformatics (Computational Biology, 2, Band 2) - Paperback

2007, ISBN: 9781402001369

Springer, Taschenbuch, Auflage: Softcover reprint of the original 1st ed. 2001, 416 Seiten, Publiziert: 2007-10-24T00:00:01Z, Produktgruppe: Buch, 2.82 kg, Programmierung & Webdesign, Com… More...

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Hidden Markov Models for Bioinformatics (Computational Biology, 2, Band 2) - T.
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T.:
Hidden Markov Models for Bioinformatics (Computational Biology, 2, Band 2) - Paperback

2007

ISBN: 9781402001369

Springer, Taschenbuch, Auflage: Softcover reprint of the original 1st ed. 2001, 416 Seiten, Publiziert: 2007-10-24T00:00:01Z, Produktgruppe: Buch, 2.82 kg, Programmierung & Webdesign, Com… More...

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Timo Koski:
Hidden Markov Models for Bioinformatics - Paperback

2001, ISBN: 1402001363

[EAN: 9781402001369], Nouveau livre, [SC: 8.08], [PU: Springer-Verlag New York Inc.], This item is printed on demand. New copy - Usually dispatched within 5-9 working days, Books

NEW BOOK. Shipping costs: EUR 8.08 THE SAINT BOOKSTORE, Southport, United Kingdom [51194787] [Note: 5 (sur 5)]
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T.:
Hidden Markov Models for Bioinformatics (Computational Biology, 2) - Paperback

ISBN: 9781402001369

paperback. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book., 2.5

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Bibliographic data of the best matching book

Details of the book
Hidden Markov Models for Bioinformatics
Author:
Title:
ISBN:

1402001363


The purpose of this book is to give a thorough and systematic introduction to probabilistic modeling in bioinformatics. The book contains a mathematically strict and extensive presentation of the kind of probabilistic models that have turned out to be useful in genome analysis. Questions of parametric inference, selection between model families, and various architectures are treated. Several examples are given of known architectures (e.g., profile HMM) used in genome analysis. Audience: This book will be of interest to advanced undergraduate and graduate students with a fairly limited background in probability theory, but otherwise well trained in mathematics and already familiar with at least some of the techniques of algorithmic sequence analysis.

Details of the book - Hidden Markov Models for Bioinformatics


EAN (ISBN-13): 9781402001369
ISBN (ISBN-10): 1402001363
Paperback
Publishing year: 2007
Publisher: Springer-Verlag New York Inc.
416 Pages
Weight: 0,620 kg
Language: eng/Englisch

Book in our database since 2008-01-30T06:11:56-05:00 (New York)
Detail page last modified on 2023-07-04T07:44:34-04:00 (New York)
ISBN/EAN: 1402001363

ISBN - alternate spelling:
1-4020-0136-3, 978-1-4020-0136-9
Alternate spelling and related search-keywords:
Book author: koski
Book title: hidden markov models for bioinformatics, hidden value

Information from Publisher

Author: T. Koski
Title: Computational Biology; Hidden Markov Models for Bioinformatics
Publisher: Springer; Springer Netherland
391 Pages
Publishing year: 2001-11-30
Dordrecht; NL
Language: English
149,79 € (DE)
153,99 € (AT)
165,50 CHF (CH)
Available
XVII, 391 p.

BC; Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Verstehen; CON_D023; Statistics; Mathematical and Computational Biology; Artificial Intelligence; Animal Anatomy; DV-gestützte Biologie/Bioinformatik; Künstliche Intelligenz; Zoologie und Tierwissenschaften; BB; EA

1 Prerequisites in probability calculus.- 2 Information and the Kullback Distance.- 3 Probabilistic Models and Learning.- 4 EM Algorithm.- 5 Alignment and Scoring.- 6 Mixture Models and Profiles.- 7 Markov Chains.- 8 Learning of Markov Chains.- 9 Markovian Models for DNA sequences.- 10 Hidden Markov Models an Overview.- 11 HMM for DNA Sequences.- 12 Left to Right HMM for Sequences.- 13 Derin’s Algorithm.- 14 Forward—Backward Algorithm.- 15 Baum—Welch Learning Algorithm.- 16 Limit Points of Baum-Welch.- 17 Asymptotics of Learning.- 18 Full Probabilistic HMM.

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