Welcome to our tutorial for developing and using a hidden Markov model (HMM)! Here, you'll find...

• The slides explaining a theory of HMMs, probability, the Markov assumption, and the major algorithms (HMM_week2.pdf)
• A basic guide to python that reviews the skills and data structures used (Intro_to_Python_Full.ipynb)
• A notebook to build your HMM either on your own (HMM_Building) or with the details filled in (HMM_Building_Key)
• The raw training data you'll need to read in (HMM_DNA_training.npy and HMM_State_training.npy)
• The counts returned by your model... which you can read in mid-way if your function is taking a while to run (emission_counts.npy, start_counts.npy, transition_counts.npy)

To access through Binder (which doesn't require any download of python or jupyter notebook), please click the Binder link. Note that if you refresh the page, your work will be lost; we recommend exporting your page if you wish to save it.

Thanks to Agara Bio, a community lab at Johns Hopkins, for organizing: https://www.agarabio.org/ and our lab for help and support https://mccoy-lab.org/

Please reach out to us any time with questions or comments! Sara Carioscia ([email protected]) Dylan Taylor ([email protected])