000			01176nam a2200145Ia 4500
008			150831s9999 xx 000 0 und d
020			_a1402001363
082			_a572.8/633 KOS-H
245			_aHidden Markov Models for Bioinformatics
250			_a1
300			_a416
520			_aThe 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.
650			_aNucleotide sequence -Mathematical models. Nucleotide sequence -Statistical methods. Markov processes.
942			_cBK
999			_c293709 _d293709