Abschnittsübersicht
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LECTURES N. 33, 34, 35 and 36
Prior and posterior probabilities (key formula10.3), pseudocounts (key formula 10.11)
Hidden Markov Models(HMM): basic structure (HA 10.3, see also Chap3_Durbin_Biological_Sequence_Analysis)
HMM Problems:Evaluation, Decoding, Learning (see slides: Introduction_Hidden_Markov_Models.pdf)
Decoding problem: the Viterbi algorithm (HA box 10.2) Training supervised/unsupervised of a HMM on a gapless profile associated to a protein family: Viterbi (minimum action path) vs Baum-Welsch (path integral) method (HA 10.3.3).
Forward/Backwards algorithms.-
Teilnehmer/innen müssenAls erledigt kennzeichnen
Slides of the lectures on the structure and training of Hidden Markov Models
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