Partially Hidden Markov Models

Søren Otto Forchhammer; Jorma Rissanen

doi:10.1109/18.508852

Partially Hidden Markov Models

Søren Otto Forchhammer
, Jorma Rissanen

IBM Research

Research output: Contribution to journal › Journal article › Research › peer-review

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
DOIs	https://doi.org/10.1109/18.508852
Publication status	Published - 1996

Access to Document

10.1109/18.508852

OpenUrl availability

Check availability in DTU Findit

Cite this

@article{5ef3578f4ea54cc99fe32676cad767ca,

title = "Partially Hidden Markov Models",

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.",

author = "Forchhammer, \{S{\o}ren Otto\} and Jorma Rissanen",

year = "1996",

doi = "10.1109/18.508852",

language = "English",

volume = "42",

pages = "1253--1256",

journal = "I E E E Transactions on Information Theory",

issn = "0018-9448",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "4",

}

TY - JOUR

T1 - Partially Hidden Markov Models

AU - Forchhammer, Søren Otto

AU - Rissanen, Jorma

PY - 1996

Y1 - 1996

N2 - 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.

AB - 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.

U2 - 10.1109/18.508852

DO - 10.1109/18.508852

M3 - Journal article

SN - 0018-9448

VL - 42

SP - 1253

EP - 1256

JO - I E E E Transactions on Information Theory

JF - I E E E Transactions on Information Theory

IS - 4

ER -

Partially Hidden Markov Models

Abstract

Access to Document

OpenUrl availability

Fingerprint

Cite this