Hidden Markov Model

A Hidden Markov Model is used to capture how unobserved states evolve in time-dependent or sequential data. It has historically played an important role in speech recognition, biological sequences, user behavior analysis, and process modeling. Its key strength lies in modeling latent-state structure behind observed outputs. However, for more complex sequence problems, it should be compared carefully against modern deep learning approaches.