Individuals, relations and structures in probabilistic models

James Cussens

Individuals, relations and structures in probabilistic models

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Relational data is equivalent to non-relational structured data. It is this equivalence which permits probabilistic models of relational data. Learning of probabilistic models for relational data is possible because one item of structured data is generally equivalent to many related data items. Succession and inclusion are two relations that have been well explored in the statistical literature. A description of the relevant statistical approaches is given. The representation of relational data via Bayesian nets is examined, and compared with PRMs. The paper ends with some cursory remarks on structured objects.

Original language	English
Title of host publication	IJCAI Workshop on Learning Statistical Models from Relational Data (SRL2003)
Editors	Lise Getoor, David Jensen
Pages	32-36
Number of pages	5
Publication status	Published - 1 Aug 2003

Access to Document

ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/srl03.pdf

Cite this

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