Hidden Markov Model
The Hidden Markov Model (HMM) is a relatively simple way to model sequential data. A hidden Markov model implies that the Markov Model underlying the data is hidden or unknown to you. More specifically, you only know observational data and not information about the states. In other words, there’s a specific type of model that produces the data (a Markov Model) but you don’t know what processes are producing it. You basically use your knowledge of Markov Models to make an educated guess about the model’s structure. (coppied)