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Research Centre
Data - Algorithms - Decision Making
Data - Algorithms - Decision Making
Established in 2005 under support of MŠMT ČR (project 1M0572)
Lectures and Presetations
Characterizing Markov equivalence classes for graphical models.
Lecturer:
Perlman M.D. (University of Washington, Seattle, USA)
From:
Sep. 13 2005 2:00PM
To:
Sep. 13 2005 3:30PM
Place:
ÚTIA AV ČR, m.č. 474
Description:
souhrn:
Chain graphs (= adicyclic graphs) have both undirected and directed edges and can be used to represent simultaneously both structural and associative dependences. Like acyclic directed graphs (ADGs = DAGs), the chain graph associated with a given statistical model may not be unique, so chain graphs fall into Markov equivalence classes, which may be super-exponentially large, leading to possible ambiguity and computational inefficiency in model search and selection. It is shown here that under the Andersson-Madigan-Perlman (AMP) Markov interpretation of a chain graph, each Markov-equivalence class can be uniquely represented by a single chain graph, the AMP essential graph that is itself simultaneously Markov equivalent to all chain graphs in the AMP Markov equivalence class. A complete characterization of AMP essential graphs is obtained.
Chain graphs (= adicyclic graphs) have both undirected and directed edges and can be used to represent simultaneously both structural and associative dependences. Like acyclic directed graphs (ADGs = DAGs), the chain graph associated with a given statistical model may not be unique, so chain graphs fall into Markov equivalence classes, which may be super-exponentially large, leading to possible ambiguity and computational inefficiency in model search and selection. It is shown here that under the Andersson-Madigan-Perlman (AMP) Markov interpretation of a chain graph, each Markov-equivalence class can be uniquely represented by a single chain graph, the AMP essential graph that is itself simultaneously Markov equivalent to all chain graphs in the AMP Markov equivalence class. A complete characterization of AMP essential graphs is obtained.
Contact person:
Within the event:
Pravidelný seminář Inteligentní systémy (Contact person: Tomáš Kroupa)
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