Partially hidden Markov models
Partially hidden Markov models (PHMM) are introduced. They differ from the ordinary HMMs 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.