Probabilistic models for relational data

Probabilistic models for relational data (2004)

by David Heckerman , Christopher Meek , Daphne Koller

Citations:

39 - 0 self

Datum	Value	Source
TITLE	Probabilistic models for relational data	INFERENCE
AUTHOR NAME	David Heckerman	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research and ∗Stanford University	SVM HeaderParse 0.2
AUTHOR NAME	Christopher Meek	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research and ∗Stanford University	SVM HeaderParse 0.2
AUTHOR NAME	Daphne Koller	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research and ∗Stanford University	SVM HeaderParse 0.2
ABSTRACT	We introduce a graphical language for relational data called the probabilistic entityrelationship (PER) model. The model is an extension of the entity-relationship model, a common model for the abstract representation of database structure. We concentrate on the directed version of this model—the directed acyclic probabilistic entity-relationship (DAPER) model. The DAPER model is closely related to the plate model and the probabilistic relational model (PRM), existing models for relational data. The DAPER model is more expressive than either existing model, and also helps to demonstrate their similarity. In addition to describing the new language, we discuss important facets of modeling relational data, including the use of restricted relationships, self relationships, and probabilistic relationships. Many examples are provided.	SVM HeaderParse 0.2
YEAR	2004	SVM HeaderParse 0.2
VENUE TYPE	TECHREPORT	INFERENCE
TECH	Technical Report MSR-TR-2004-30, Microsoft Research	INFERENCE
CITATIONS	20 found	ParsCit 1.0