A Hidden Markov Model (HMM) is a statistical model that represents systems where the state is not directly observable but can be inferred through observable outputs. It consists of a set of hidden states, observable events, transition probabilities between states, and emission probabilities for producing observations. This model is especially useful in applications like biological sequence analysis and speech recognition, where the system's internal states are unknown but can be inferred from the data.
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