HMM Meaning and
Definition
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HMM is an acronym for Hidden Markov Model. It is a statistical model used in pattern recognition and machine learning tasks, particularly in analyzing sequential data.
A Hidden Markov Model consists of a set of states, each representing a particular situation or condition, and a set of observations or outputs, which are related to those states. However, the states themselves are not directly observable; they are considered hidden or latent variables. The transitions between states are governed by a set of probabilities, where the probability of transitioning from one state to another depends only on the current state. Similarly, the observations also have associated probabilities depending on the corresponding underlying state.
HMM can be represented through a graphical model, where the states are represented as nodes and the transitions and observation probabilities are represented as edges between them. By training an HMM on a large dataset with known states and corresponding observations, it can learn the probabilities of different transitions and observations, enabling it to make predictions or estimations on new sequences or observations.
HMM finds applications in various fields, including speech recognition, handwriting recognition, bioinformatics, and natural language processing. Its ability to learn from sequential data and handle uncertainty makes it a powerful tool for modeling complex systems and predicting future outcomes.
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