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 an HMM, the system is in one of several states at any given time, but these states are not directly observable. Instead, the model produces observable outputs that depend on the hidden states, allowing for the inference of the underlying state sequence based on the observed data. HMMs are widely used in various fields, including speech recognition, bioinformatics, and finance. They are particularly useful for tasks involving time series data, where the goal is to predict future states or classify sequences based on observed patterns.