Hidden Markov Models

Hidden Markov Models (HMMs) are statistical models that represent systems with hidden states. They are characterized by their ability to model temporal sequences and are often used in scenarios where the system being observed is not directly visible. HMMs are widely applied in various fields, including speech recognition, bioinformatics, and finance, where they can be used to predict future states based on observed data. The model consists of states, transitions between states, and emission probabilities that define the likelihood of observing certain outputs given a particular state. Their effectiveness in dealing with sequential data makes them a fundamental tool in machine learning and data analysis.