Notes on Hidden Markov Models
نویسنده
چکیده
A Markov model, or Markov chain, can be viewed as a stochastic finite state automaton in which each transition is labeled with a probability in such a way that the set of transitions from a given state have their probability labels adding up to 1. If in addition each state is labeled with an observable symbol, the probability of a string of symbols [x1, x2, ..., xn] (abbreviated as x1,n) is the product of the probabilities of successive transitions (starting in state x0, an arbitrary label for the string beginning).
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