Section outline

◄11. CLUSTER ANALYSIS
►13. HIDDEN MARKOV MODELS

  • LECTURES N. 28, 29 and 30

    Relevance of Bayes’ theorem in the analysis of sequences

    (HA 10.2, bayesian model (parameter) selection: pseudo counts

    generative probabilistic models

    Markov order 0 models (urn models)

    A bayesian classifier of disordered proteins ( a critique of, look at the priors (Bulashevska2008)

    Entropy rules from Baldi and Brunak)
    Bayes Factors in model selection: the relevance of priors (see HA  eq. 10.5 and box 10.1)

    STUDY MATERIALS AND SUGGESTIONS FOR PERSONAL STUDY IN CB-23_24_PACK_11

    • CB_23_24_PACK_11 File
      Students must
      Mark as done

      Find here the slides that were used to introduce the discussion and materials for personal study

◄11. CLUSTER ANALYSIS
►13. HIDDEN MARKOV MODELS