22/03/2018 – Hidden Markov Models
Thursday 22/03/2018 at 15:00 in room B1.09
In this meeting, Emmeke will introduce us to Hidden Markov Models.
The HMM is a very flexible model and as such is applicable to a wide variety of longitudinally collected data. For example, one can extract student behaviour states from MOOC data and investigate the composition of the different learning states, and the transitions between the different learning states. Or one can extract sleep states based on EEG measurements, and subsequently compare the duration of, and transitions between, different sleep states for patients which do and do not suffer from insomnia.
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