Hidden Markov Model

A Hidden Markov Model (HMM) is a statistical model that is used to describe a system with unobserved states that are inferred from observed data. It is a type of Markov chain where the states are not directly observable, but instead generate a sequence of observations. HMMs are widely used in speech recognition, natural language processing, bioinformatics, and other fields where sequential data is encountered. The model consists of a set of states, a set of observations, and a set of probabilities that describe the transitions between states and the emission of observations from each state. The parameters of the model are typically learned from training data using the Baum-Welch algorithm or other similar methods.
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