Heinz Koeppl, Marc Hafner, et al.
BMC Bioinformatics
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. © 1996 IEEE.
Heinz Koeppl, Marc Hafner, et al.
BMC Bioinformatics
Robert E. Donovan
INTERSPEECH - Eurospeech 2001
Rajeev Gupta, Shourya Roy, et al.
ICAC 2006
Thomas M. Cover
IEEE Trans. Inf. Theory