Overview

The purpose of this book is to give a thorough and systematic introduction to probabilistic modelling 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 (for example, profile HMM) used in genome analysis.

Full Product Details

Author:   T. Koski
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2002 ed.
Volume:   2
Dimensions:   Width: 15.50cm , Height: 2.30cm , Length: 23.50cm
Weight:   1.670kg
ISBN:  

9781402001352

ISBN 10:   1402001355
Pages:   391
Publication Date:   30 November 2001
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of print, replaced by POD  
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Table of Contents

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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