A Markov model is a stochastic model used to model pseudo-randomly changing systems. Markov and hidden Markov models are used as a mathematical tool. It is assumed that future states depend only on the current state, not on the events that occurred before it. A hidden Markov model is a Markov chain for which the state is only partially observable or noisily observable. Markov model is a state machine with the state changes being probabilities. HMM may generally be used in pattern recognition problems, anywhere there may be a model producing a sequence of observations. In a hidden Markov model, you don’t know the probabilities, but you know the outcomes. For example, when you flip a coin, you can get the probabilities, but, if you couldn’t see the flips and someone moves one of five fingers with each coin flip, you could take the finger movements and use a hidden Markov model to get the best guess of coin flips.

Shashank Shekhar Pathak

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