Hidden Markov model

Hidden Markov Models (HMMs) are a way of predicting what might happen next, based on what has happened in the past. Think of HMMs like a game of hide-and-seek. In each turn, you are looking for something that is hidden. It might be a toy, a hug, or something else. The game goes on until you find the hidden thing.

In the same way, HMMs use information from the past to try to predict what will happen in the future. They do this by looking at the “hidden” things that could be happening. For example, if you have a weather forecasting system, the “hidden” thing could be the weather in the near future. The system might look at the past weather data to see what the patterns are and then use that information to predict the weather in the near future.

So in summary, HMMs are like a game of hide-and-seek where you use information from the past to try and predict what will happen in the future.