Learning Multivariate Regression Chain Graphs under Faithfulness
2012 (English)In: Proceedings of the 6th European Workshop on Probabilistic Graphical Models, Granada (Spain), 19-21 September, 2012 / [ed] Andrés Cano, Manuel Gémez.-Olmedo and Thomas D. Nielsen, 2012, 299-306 p.Conference paper (Refereed)
This paper deals with multivariate regression chain graphs, which were introduced by Cox and Wermuth (1993, 1996) to represent linear causal models with correlated errors. Specifically, we present a constraint based algorithm for learning a chain graph a given probability distribution is faithful to. We also show that for each Markov equivalence class of multivariate regression chain graphs there exists a set of chain graphs with a unique minimal set of lines. Finally, we show that this set of lines can be identified from any member of the class by repeatedly splitting its connectivity components according to certain conditions.
Place, publisher, year, edition, pages
2012. 299-306 p.
Chain Graph, Multivariate Regression Chain Graph, Learning, Bidirected Graph
IdentifiersURN: urn:nbn:se:liu:diva-80306ISBN: 978-84-15536-57-4OAI: oai:DiVA.org:liu-80306DiVA: diva2:546468
Sixth European Workshop on Probabilistic Graphical Models (PGM 2012), 19-21 September 2012, Granada, Spain