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Hidden Markov Models for Bioinformatics / Edition 1
     

Hidden Markov Models for Bioinformatics / Edition 1

by T. Koski
 

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ISBN-10: 1402001363

ISBN-13: 9781402001369

Pub. Date: 11/30/2001

Publisher: Springer Netherlands

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

Overview

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.

Product Details

ISBN-13:
9781402001369
Publisher:
Springer Netherlands
Publication date:
11/30/2001
Series:
Computational Biology Series , #2
Edition description:
Softcover reprint of the original 1st ed. 2001
Pages:
391
Product dimensions:
9.21(w) x 6.14(h) x 0.85(d)

Related Subjects

Table of Contents

Foreword. 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. Index.

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