{pdf} Hidden Markov Models of Bioinformatics Timo Koski

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