Publication
IEEE Trans. Inf. Theory
Paper

Partially Hidden Markov models

View publication

Abstract

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.

Date

Publication

IEEE Trans. Inf. Theory

Authors

Topics

Share