Bayesian Model Averaging And Model Selection For Markov Equivalence Classes Of Acyclic Digraphs

Bayesian Model Averaging And Model Selection For Markov Equivalence Classes Of Acyclic Digraphs (1996)

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by David Madigan , Steen Andersson , Michael Perlman , Chris Volinsky

Venue:	Communications in Statistics: Theory and Methods
Citations:	35 - 5 self

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Datum	Value	Source
TITLE	Bayesian Model Averaging And Model Selection For Markov Equivalence Classes Of Acyclic Digraphs	user correction - Legacy Corrections
AUTHOR NAME	David Madigan	user correction - Legacy Corrections
AUTHOR AFFIL	1	user correction - Legacy Corrections
AUTHOR NAME	Steen Andersson	user correction - Legacy Corrections
AUTHOR AFFIL	2	user correction - Legacy Corrections
AUTHOR NAME	Michael Perlman	user correction - Legacy Corrections
AUTHOR AFFIL	1	user correction - Legacy Corrections
AUTHOR NAME	Chris Volinsky	user correction - Legacy Corrections
AUTHOR AFFIL	; 1; 1; Department of Statistics; University of Washington,; Indiana University,	user correction - Legacy Corrections
AUTHOR ADDR	; Box 354322,; Seattle, WA 98195, USA.; Bloomington, IN 47405, USA.	user correction - Legacy Corrections
ABSTRACT	Acyclic digraphs (ADGs) are widely used to describe dependences among variables in multivariate distributions. In particular, the likelihood functions of ADG models admit convenient recursive factorizations that often allow explicit maximum likelihood estimates and that are well suited to building Bayesian networks for expert systems. There may, however, be many ADGs that determine the same dependence (= Markov) model. Thus, the family of all ADGs with a given set of vertices is naturally partitioned into Markov-equivalence classes, each class being associated with a unique statistical model. Statistical procedures, such as model selection or model averaging, that fail to take into account these equivalence classes, may incur substantial computational or other inefficiencies. Recent results have shown that each Markov-equivalence class is uniquely determined by a single chain graph, the essential graph, that is itself Markov-equivalent simultaneously to all ADGs in the equivalence clas...	user correction - Legacy Corrections
YEAR	1996	INFERENCE
VENUE	Communications in Statistics: Theory and Methods	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	2493--2519	INFERENCE
VOLUME	25	INFERENCE
CITATIONS	36 found	ParsCit 1.0