Rolf Clauberg
IBM J. Res. Dev
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.
Rolf Clauberg
IBM J. Res. Dev
Leo Liberti, James Ostrowski
Journal of Global Optimization
Thomas M. Cover
IEEE Trans. Inf. Theory
Corneliu Constantinescu
SPIE Optical Engineering + Applications 2009