Computational Biology
Hidden Markov Models
Hidden Markov models. Rosalind
Solve the exercises of the HMM track on Rosalind. We will use the matrix visualization developed during the project to add the remaining exercises to Dodona with newly generated test cases and sometimes with some slight modifications to the problem statement. Solutions to these exercises will be presented by the students in class. You might find some inspiration about using HMM in the following Jupyter Notebook demos:
| Status | Status | Type | Titel | Voortgang groep |
|---|---|---|---|---|
| Probability of a hidden path | ||||
| Probability of an outcome given a hidden path | ||||
| Viterbi decoding | ||||
| Outcome likelihood |