Hidden Markov Model (HMM)
A Hidden Markov Model (HMM) is a powerful statistical tool used in various fields, including artificial intelligence, speech recognition, and bioinformatics. It is particularly useful for modeling systems that exhibit a sequence of observable events influenced by internal states that are not directly visible (hence ‘hidden’).
At its core, an HMM consists of two main components: a set of hidden states and a set of observable events. The model assumes that the system transitions between these hidden states according to certain probabilities, and each hidden state produces observable events based on specific emission probabilities.
The key features of HMMs include:
- States: The underlying states of the system, which are not directly observable but can be inferred from the observed data.
- Observations: The events or outputs that can be seen and measured, which provide clues about the hidden states.
- Transition Probabilities: The probabilities of moving from one hidden state to another, which inform how the system evolves over time.
- Emission Probabilities: The probabilities of observing certain events given a specific hidden state.
HMMs are commonly trained using algorithms such as the Baum-Welch algorithm or the Viterbi algorithm, which help estimate the model parameters and find the most likely sequence of hidden states given the observed data. Applications of HMMs span across various domains, including natural language processing, where they help in part-of-speech tagging, and in finance for modeling stock prices.