HMM (Hidden Markov Model)

A Hidden Markov Model (HMM) is a statistical tool used to analyze systems that have unobserved (hidden) states affecting observable outcomes. Imagine it as a process where you can't see the internal state directly but can observe results influenced by it. The model assumes the system moves through a sequence of such states, with probabilities governing how it transitions from one to another. By analyzing the visible data, HMMs help infer the most likely sequence of hidden states, enabling applications like speech recognition, bioinformatics, and language processing to understand underlying patterns behind observable signals.