HMM (Hidden Markov Models)

Hidden Markov Models (HMMs) are statistical tools used to predict patterns when you can observe some data but can't directly see the underlying process. Imagine trying to determine a person's mood (happy, sad, excited) based only on their facial expressions and voice, without knowing their true feelings. The emotions are "hidden," but the observations give clues. HMMs model this by assuming the system moves between unknown states over time, with observable outputs linked to those states. They're widely used in speech recognition, bioinformatics, and other areas where indirect measurements help infer hidden sequences.