Hidden Markov Models Explained with a Real Life Example and Python code

Hidden Markov Models are probabilistic models used to solve real life problems ranging from something everyone thinks about at least once a week — how is the weather going to be like tomorrow?[1] — to hard molecular biology problems, such as predicting peptide binders to the human MHC class II molecule[2].

Hidden Markov Models are close relatives of Markov Chains, but their hidden states make them a unique tool to use when you’re interested in …

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