Hidden Markov Model
Hidden Markov Model
A Hidden Markov Model (HMM) is a statistical model that assumes the system being modeled exists in a set of hidden states, and these hidden states can only be observed through a set of visible states. HMMs are commonly used to model sequential data, such as speech or text, where the hidden states represent the underlying structure or meaning of the data.
What does Hidden Markov Model mean?
A Hidden Markov Model (HMM) is a statistical model that represents a system with unobserved or “hidden” states. These states emit observable outputs, and the model’s goal is to determine the most likely sequence of hidden states given a sequence of observations.
HMMs consist of three main components:
- States: The unobserved states that represent the system’s hidden structure.
- Observations: The observable outputs produced by the system.
- Transition and Emission Probabilities: Matrices that define the probability of transitioning between states and the probability of emitting observations from each State.
The key concept in HMMs is that the Current state depends only on the previous state, and the observations depend only on the current state. This is known as the Markov assumption. It allows HMMs to model systems with sequential dependencies without the need for complex computational models.
HMMs are used in a wide range of applications, including:
- Speech recognition
- Natural language Processing
- Bioinformatics
- Financial modeling
- Robotics
Applications
HMMs are particularly useful in applications where the observed data is sequential and the underlying system is hidden or partially observable. For example, in speech recognition, the observed data is a sequence of acoustic signals, and the hidden states represent the sequence of phonemes or words being spoken. HMMs can be used to determine the most likely sequence of phonemes or words based on the acoustic signals.
In natural language processing, HMMs can be used to Tag parts of speech or identify named entities. In bioinformatics, HMMs can be used to identify gene sequences or protein structures. In financial modeling, HMMs can be used to predict Stock prices or economic trends. In robotics, HMMs can be used to control robot movements or interpret sensory data.
History
The concept of HMMs was first developed by Andrey Markov in the early 1900s. Markov’s models were used to describe the behavior of random walks, but they were later extended to more complex systems. In the 1960s, Leonard Baum and Lloyd Welch developed a set of algorithms for training and decoding HMMs, which made them more practical for real-world applications.
Today, HMMs are widely used in a variety of fields. They have proven to be a powerful tool for modeling sequential data and extracting meaningful information from complex systems.