Hidden Markov models for bioinformatics / by Timo Koski.

Format:

Book

Author/Creator:

Koski, Timo.

Series:

Computational biology ; v. 2.

Computational biology ; v. 2

Language:

English

Subjects (All):

Nucleotide sequence--Mathematical models.

Nucleotide sequence.

Nucleotide sequence--Statistical methods.

Markov processes.

Base Sequence--models.

Base Sequence.

Markov Chains.

Medical Subjects:

Base Sequence--models.

Base Sequence.

Markov Chains.

Physical Description:

xvii, 391 pages : illustrations ; 25 cm.

Place of Publication:

Dordrecht ; Boston : Kluwer Academic Publishers, [2001]

Summary:

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.

Notes:

Includes bibliographical references and index.

ISBN:

1402001355

OCLC:

48170645