Hidden Markov Models (HMMs) are a type of statistical model used to describe sequences of observations where the underlying system being observed is assumed to be a Markov process with unobservable (hidden) states that evolve over time. HMMs are commonly used in a wide range of applications including speech recognition, natural language processing, bioinformatics, and financial modeling. In an HMM, the hidden states are connected in a directed graph where transition probabilities exist between states. Observations are emitted based on the current state, with emission probabilities specifying the likelihood of each observation given the state. The goal of using HMMs is to determine the most likely sequence of hidden states given a sequence of observations, known as the Viterbi algorithm. HMMs are a powerful tool for modeling sequential data and have the advantage of being able to capture complex dependencies between observations, making them widely used in various fields of research and application.