Partially Hidden Markov Models

Søren Otto Forchhammer, Jorma Rissanen

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    Partially Hidden Markov Models (PHMM) are introduced. They differ from the ordinary HMM's in that both the transition probabilities of the hidden states and the output probabilities are conditioned on past observations. As an illustration they are applied to black and white image compression where the hidden variables may be interpreted as representing noncausal pixels.
    Original languageEnglish
    JournalI E E E Transactions on Information Theory
    Issue number4
    Pages (from-to)1253-1256
    Publication statusPublished - 1996

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