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Hidden Markov Models of Bioinformatics

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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

Author(s): Timo Koski
Series: Computational Biology
Edition: 1
Publisher: Springer
Year: 2002

Language: English
Pages: 404
Tags: Биологические дисциплины;Матметоды и моделирование в биологии;Биоинформатика;