Hidden Markov Model

CORE DEFINITION

The Hidden Markov Model (HMM) is a statistical model that describes a Markov process with hidden unknown parameters. The difficulty lies in determining the hidden parameters of the process from observable parameters, which are then used for further analysis such as pattern recognition. In a regular Markov model, the states are directly visible to the observer, and thus the state transition probabilities are the only parameters. In a hidden Markov model, the states are not directly visible, but variables influenced by the states are visible. Each state has a probability distribution over possible output symbols. Therefore, the sequence of output symbols can reveal some information about the state sequence. Hidden Markov models are applied in thermodynamics, statistical mechanics, physics, chemistry, economics, finance, signal processing, information theory, pattern recognition (such as speech recognition, handwriting recognition, gesture recognition, part-of-speech tagging, and music score following), partial discharge, and bioinformatics.