What is hmm?

Hidden Markov Model (HMM) is a statistical model that is used to model some sequence data where the data is assumed to be generated by an underlying process that is not visible to the observer. HMM is used to model problems in speech recognition, natural language processing, bioinformatics, and reinforcement learning.

In an HMM model, the underlying process is modeled by a Markov process where the underlying states are unobservable. The observed sequence is generated by selecting one of the underlying states according to a probability distribution, and then generating an observation according to another probability distribution.

The model is called "hidden" because the states are not directly observable, and the model must infer them from the observed data. HMM is trained by estimating the model parameters from a training dataset using the Baum–Welch algorithm, and the model can be used to perform inference, such as predicting the next observation given a sequence of observed data.

HMM has several variants, such as discrete HMM and continuous HMM, and is widely used in various applications.