Hidden Markov Model (HMM): Components and Working
A Hidden Markov Model (HMM) is a statistical model used to represent systems that are probabilistic and time-dependent, especially when the system being modeled is hidden (not directly observable), but its outputs are visible. HMM is widely used in applications like speech recognition, bioinformatics, and handwriting recognition.
Components of HMM:
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States: The model consists of a set of hidden states. These states are not directly observable.
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Observations: These are the visible outputs or observations generated by the hidden states.
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Transition Probabilities: The probability of moving from one state to another.
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Emission Probabilities: The probability of an observation being generated from a particular state.
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Initial Probabilities: The probability of starting in each state.
Working of HMM:
HMM works by assuming that at each time step, the system is in some hidden state which changes according to the transition probabilities. Each state produces an observable output based on the emission probabilities. Using algorithms like Forward-Backward, Viterbi, and Baum-Welch, HMM can decode the most likely sequence of hidden states or learn model parameters.
Conclusion:
HMM is a powerful tool for modeling sequential data where the actual process is hidden but can be inferred from observed data.
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