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.
| Original language | English |
|---|---|
| Journal | I E E E Transactions on Information Theory |
| Volume | 42 |
| Issue number | 4 |
| Pages (from-to) | 1253-1256 |
| ISSN | 0018-9448 |
| Publication status | Published - 1996 |