Searching for Bayesian Network Structures in the Space of Restricted Acyclic Aprtially Directed Graphs

Searching for Bayesian Network Structures in the Space of Restricted Acyclic Aprtially Directed Graphs (2003)

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by Silva Acid , Luis M. de Campos

Venue:	Journal of Artificial Intelligence Research
Citations:	12 - 1 self

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Datum	Value	Source
TITLE	Searching for Bayesian Network Structures in the Space of Restricted Acyclic Aprtially Directed Graphs	user correction - Legacy Corrections
AUTHOR NAME	Silva Acid	user correction - Legacy Corrections
AUTHOR NAME	Luis M. de Campos	user correction - Legacy Corrections
AUTHOR AFFIL	Departamento de Ciencias de la Computacion e I.A.; E.T.S.I. Informatica, Universidad de Granada	user correction - Legacy Corrections
AUTHOR ADDR	18071- Granada, SPAIN	user correction - Legacy Corrections
ABSTRACT	Although many algorithms have been designed to construct Bayesian network structures using dierent approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). Within the score+search paradigm, the dominant approach uses local search methods in the space of directed acyclic graphs (DAGs), where the usual choices for de ning the elementary modi cations (local changes) that can be applied are arc addition, arc deletion, and arc reversal. In this paper, we propose a new local search method that uses a dierent search space, and which takes account of the concept of equivalence between network structures: restricted acyclic partially directed graphs (RPDAGs). In this way, the number of dierent con gurations of the search space is reduced, thus improving eciency. Moreover, although the nal result must necessarily be a local optimum given the nature of the search method, the topology of the new search space, which avoids making early decisions about the directions of the arcs, may help to nd better local optima than those obtained by searching in the DAG space.	user correction - Legacy Corrections
YEAR	2003	INFERENCE
VENUE	Journal of Artificial Intelligence Research	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	445--490	INFERENCE
VOLUME	18	INFERENCE
CITATIONS	64 found	ParsCit 1.0