hidden Markov model

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Definitions:

A Markov model in which the system being modeled is assumed to be a Markov process with unobservable states. The model provides an observable process whose outcomes are influenced by the outcomes of a Markov model in a known way. An HMM can be used to describe the evolution of observable events that depend on internal factors that are not directly observable. In machine learning, it is assumed that the internal state of a model is hidden but not its hyperparameters.
Sources:
NIST AI 100-2e2025

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