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A hidden Markov random field is a generalization of a hidden Markov model. Instead of having an underlying Markov chain, hidden Markov random fields have an underlying Markov random field. Suppose that we observe a random variable , where . Hidden Markov random fields assume that the probabilistic nature of is determined by the unobservable Markov random field , . That is, given the neighbors of , is independent of all other (Markov property). The main difference with a hidden Markov model is that neighborhood is not defined in 1 dimension but within a network, i.e. is allowed to have more than the two neighbors that it would have in a Markov chain. The model is formulated in such a way that given , are independent (conditional independence of the observable variables given the Markov random field). == See also == * Hidden Markov model * Markov network * Bayesian network 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Hidden Markov random field」の詳細全文を読む スポンサード リンク
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