Hidden Markov Models for Bioinformatics

Specificaties
Paperback, 391 blz. | Engels
Springer Netherlands | 0e druk, 2001
ISBN13: 9781402001369
Rubricering
Springer Netherlands 0e druk, 2001 9781402001369
Onderdeel van serie Computational Biology
Verwachte levertijd ongeveer 8 werkdagen

Samenvatting

This text is based on a set of not es produced for courses given for gradu­ ate students in mathematics, computer science and biochemistry during the academic year 1998-1999 at the University of Turku in Turku and at the Royal Institute of Technology (KTH) in Stockholm. The course in Turku was organized by Professor Mats Gyllenberg's groupl and was also included 2 within the postgraduate program ComBi , a Graduate School in Compu­ tational Biology, Bioinformatics, and Biometry, directed by Professor Esko Ukkonen at the University of Helsinki. The purpose of the courses was to give a thorough and systematic intro duc ti on to probabilistic modelling in bioinformatics for advanced undergraduate and graduate students who had a fairly limited background in prob ability theory, but were otherwise well trained in mathematics and were already familiar with at least some of the techniques of algorithmic sequence analysis. Portions of the material have also been lectured at shorter graduate courses and seminars both in Finland and in Sweden. The initial set of notes circulated also for a time outside those two countries via the World Wide Web. The intermediate course in probability theory and techniques of discrete mathematics held by the author at the University College of Södertörn (Hud­ dinge, Sweden) during the academic year 1997-1998 has also influenced the presentation. The opportunity to give this course is hereby gratefully ac­ knowledged.

Specificaties

ISBN13:9781402001369
Taal:Engels
Bindwijze:paperback
Aantal pagina's:391
Uitgever:Springer Netherlands
Druk:0

Inhoudsopgave

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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        Hidden Markov Models for Bioinformatics