Hidden Markov Models for Bioinformatics T. Koski,
Publisher: Springer
ISBN: 1402001355
Edition: Hardcover; 2001-12-15
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.
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