Hidden Markov Models (HMMs) are statistical models used to represent systems that transition between hidden states over time. Each state is associated with observable outputs, but the states themselves are not directly visible. HMMs are widely used in various fields, including speech recognition, bioinformatics, and finance, to analyze sequences of data. An HMM consists of a set of states, transition probabilities between these states, and emission probabilities that link states to observable outputs. The model uses algorithms like the Viterbi algorithm to infer the most likely sequence of hidden states based on observed data, making it a powerful tool for pattern recognition and prediction.