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Hidden Markov Models (HMMs) are utilized in various applications such as speech recognition, gene finding, and protein structure prediction. An HMM consists of states and transitions with associated probabilities, and it operates under the Markov assumption where the future state depends only on the current state. Key problems in HMMs include evaluation, decoding, and learning, which involve calculating probabilities related to sequences and model parameters.

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tuli0001

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EN