A Hidden Markov Model (HMM) is a statistical model that represents systems that are assumed to be a Markov process with hidden states. In an HMM, the system being modeled is assumed to be a process that transitions between a finite number of states, where the state itself is not directly observable (hidden), but can be inferred through observable outputs or emissions associated with those states. This connection to Markov chains highlights the importance of transition probabilities between states and the significance of emission probabilities in predicting observable events.
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