Hidden Markov Model (HMM)

hidden Markov Models are probabilistic models for linear sequence 'labeling' problems where probability distributions over sequences of observations. In Hidden Markov Model the data inputs are given to the model and the probabilities for the current state and the state immediately preceding it are used to calculate the most likely outcome. In the hidden Markov Model describes the probabilistic relationship between a sequence of observations and a sequence of hidden states. the state is not directly visible, but the output, dependent on the state, is visible.