Hidden Markov Model (HMM)

A Hidden Markov Model (HMM) is a statistical tool used to analyze systems that follow a sequence of unobservable (hidden) states, which influence observable events. Imagine it as a process where you can't directly see the states, but you can observe clues or outputs affected by those states. The model uses probabilities to predict the most likely sequence of hidden states based on the observed data, and also to determine the likelihood of certain observations. It's widely used in areas like speech recognition, natural language processing, and bioinformatics to decode underlying patterns from visible signals.