A hidden Markov model (HMM) is a kind of statistical model that is a variation on the Markov chain. In a hidden Markov model, there are "hidden" states, or unobserved, in contrast to a standard Markov chain where all states are visible to the observer. Hidden Markov models are used for machine learning and data mining tasks including speech, handwriting and gesture recognition.
The hidden Markov model was developed by the mathematician L.E. Baum and his colleagues in the 1960s. Like the popular Markov chain, the hidden Markov model attempts to predict the future state of a variable using probabilities based on the current and past state. The key difference between a Markov chain and the hidden Markov model is that the state in the latter is not directly visible to an observer, even though the output is.
Hidden Markov models are used for machine learning and data mining tasks. Some of these include speech recognition, handwriting recognition, part-of-speech tagging and bioinformatics.