An Introduction to Belief Networks

Simple item page
dc.contributor.advisorBaras, John S.en_US
dc.contributor.authorLi, Hongjunen_US
dc.contributor.departmentISRen_US
dc.contributor.departmentCSHCNen_US
dc.date.accessioned2007-05-23T10:08:28Z
dc.date.available2007-05-23T10:08:28Z
dc.date.issued1999en_US
dc.description.abstractBelief networks, also called Bayesian networks or probabilistic causal networks, were developed in the late 1970s to model the distributed processing in reading comprehension. Since then they have attracted much attention and have become popular within the AI probability and uncertainty community. As a natural and efficient model for humans' inferential reasoning, belief networks have emerged as the general knowledge representation scheme under uncertainty.<p>In this report, we first introduce belief networks in the light of knowledge representation under uncertainty, then in the remainingsections we give the descriptions of the semantics, inference mechanisms and some issues related to learning belief networks, respectively. This report is not intended to be a tutorial for beginners. Rather it aims to point out some important aspects of belief networks and summarize some important algorithms.en_US
dc.format.extent480348 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/6088
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1999-58en_US
dc.relation.ispartofseriesCSHCN; TR 1999-31en_US
dc.subjectestimationen_US
dc.subjectgraph theoryen_US
dc.subjectknowledge representationen_US
dc.subjectbelief networksen_US
dc.subjectIntelligent Signal Processing and Communications Systemsen_US
dc.subjectSystems Integration Methodologyen_US
dc.titleAn Introduction to Belief Networksen_US
dc.typeTechnical Reporten_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
TR_99-58.pdf
Size:
469.09 KB
Format:
Adobe Portable Document Format
Download

Collections

Institute for Systems Research Technical Reports