Explain the Hidden Markov model.

Hidden Markov model is a statistical model used for representing the probability distributions over a chain of observations. In the hidden markov model, hidden defines a property that it assumes that the state of a process generated at a particular time is hidden from the observer, and Markov defines that it assumes that the process satisfies the Markov property. The HMM models are mostly used for temporal data.

The HMM is used in various applications such as reinforcement learning, temporal pattern recognition, etc.