A Hidden Markov Model (HMM) is a statistical model used to represent systems that are assumed to follow a Markov process with hidden states. In this model, the system transitions between a finite number of states, but the states themselves are not directly observable. Instead, each state produces observable outputs, allowing for inference about the hidden states based on the observed data. HMMs are widely used in various fields, including speech recognition, bioinformatics, and finance. They help in tasks such as predicting sequences, classifying data, and modeling time series, making them valuable tools for analyzing complex systems where the underlying processes are not directly visible.