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Hidden Markov Model (HMM), a type of stochastic model (signal source), is now becoming popular in molecular biology. HMMs consist of 'hidden' states, statetransition probabilities and output distributions. Because there are known algorithms to train the HMMs as stochastic representations of the training data, they are widely used for pattern recognition, especially for speech recognition. In the field of protein research, HMMs have been used to represent stochastic motifs of protein sequences,doi:10.11234/gi1990.4.130 fatcat:kgzafnbg7nejpecnjx3tziwwly