Encoding probability propagation in belief networks

Zhang, S; Zhang, C

Encoding probability propagation in belief networks

Zhang, S Zhang, C

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., 2002, 32 (4), pp. 526 - 531
Issue Date:: 2002-07-01

Closed Access

	Filename	Description	Size
	2004002923.pdf		302.83 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2002-07-01	en_US
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., 2002, 32 (4), pp. 526 - 531	en_US
dc.identifier.issn	1083-4427	en_US
dc.identifier.uri	http://hdl.handle.net/10453/5788
dc.description.abstract	Complexity reduction is an important task in Bayesian networks. Recently, an approach known as the linear potential function (LPF) model has been proposed for approximating Bayesian computations. The LPF model can effectively compress a conditional probability table into a linear function. This correspondence extends the LPF model to approximate propagation in Bayesian networks. The extension focuses on encoding probability propagation as a polynomial function for a class of tractable problems.	en_US
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans.	en_US
dc.relation.isbasedon	10.1109/TSMCA.2002.804784	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Encoding probability propagation in belief networks	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	32	en_US
utslib.for	080604 Database Management	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
dc.location.activity	Tokyo, Japan
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	32	en_US

Abstract:

Complexity reduction is an important task in Bayesian networks. Recently, an approach known as the linear potential function (LPF) model has been proposed for approximating Bayesian computations. The LPF model can effectively compress a conditional probability table into a linear function. This correspondence extends the LPF model to approximate propagation in Bayesian networks. The extension focuses on encoding probability propagation as a polynomial function for a class of tractable problems.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/5788