Review:
Hidden Markov Models
overall review score: 4.5
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score is between 0 and 5
Hidden Markov Models are a statistical model that describes the probability of a sequence of observable events based on an underlying sequence of hidden states.
Key Features
- Markov property
- Hidden states
- Observable events
- Transition probabilities
- Emission probabilities
Pros
- Effective in modeling sequences with hidden states
- Widely used in speech recognition, bioinformatics, and other fields
- Can handle sequential data with dependencies
Cons
- Complex to train and interpret
- Sensitivity to parameter estimation