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Hidden Markov models

Last updated on Wednesday, April 24, 2024.

 

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Hidden Markov models (HMMs) are mathematical models used in artificial intelligence and computational biology to represent a system with unobservable (hidden) states inferred from observable data. They are used to analyze sequential data, such as speech recognition, natural language processing, and gene prediction, by modeling the probability of transitions between hidden states and the emissions of observable symbols.

The Concept of Hidden Markov Models in Computer Science

Hidden Markov Models (HMMs) are a powerful statistical tool used in artificial intelligence and machine learning applications. They are a type of stochastic model that is particularly useful for modeling time series data where the underlying system is assumed to be a Markov process with unobservable (hidden) states.

Key Components of Hidden Markov Models:

1. States: HMMs involve a system with a finite set of hidden states that the model can be in at any given time.

2. Observations: At each time step, the model emits an observation based on the current hidden state. These observations provide information about the hidden state.

3. Transition Probabilities: HMMs use transition probabilities to model the likelihood of moving from one hidden state to another.

4. Emission Probabilities: The emission probabilities determine the probability of observing a specific output based on the current hidden state.

Applications of Hidden Markov Models:

1. Speech Recognition: HMMs are widely used in speech recognition systems to model speech patterns and detect spoken words.

2. Biological Sequence Analysis: In bioinformatics, HMMs are used to analyze DNA sequences and protein structures.

3. Time Series Analysis: HMMs are effective in modeling financial data, weather patterns, and other time-dependent phenomena.

Overall, Hidden Markov Models are a versatile and powerful tool in the field of computer science, enabling researchers and practitioners to model complex systems and make predictions based on observed data.

 

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