Hidden Markov Models (HMM)

Hidden Markov Models (HMM) are statistical tools used to analyze systems that are not directly observable, where the system's true state is hidden. They consist of a series of states that can change over time, guided by probabilities. Each state generates observable outputs, which helps infer the hidden states. For example, in speech recognition, the spoken words are the outputs, while the precise phonetic states are hidden. HMMs allow us to make predictions and understand sequences in various fields, such as language processing, finance, and biology.