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Approximate Counting of Graphical Models Via MCMC Revisited
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology. (ADIT)
2013 (English)In: Advances in Artificial Intelligence, Springer Berlin/Heidelberg, 2013, 383-392 p.Conference paper (Refereed)
Abstract [en]

In [6], MCMC sampling is applied to approximately calculate the ratio of essential graphs (EGs) to directed acyclic graphs (DAGs) for up to 20 nodes. In the present paper, we extend that work from 20 to 31 nodes. We also extend that work by computing the approximate ratio of connected EGs to connected DAGs, of connected EGs to EGs, and of connected DAGs to DAGs. Furthermore, we prove that the latter ratio is asymptotically 1. We also discuss the implications of these results for learning DAGs from data.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2013. 383-392 p.
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 8109
National Category
Computer Science
URN: urn:nbn:se:liu:diva-98068DOI: 10.1007/978-3-642-40643-0_39ISI: 000340401800039ISBN: 978-3-642-40642-3 (print)ISBN: 978-3-642-40643-0 (online)OAI: diva2:651934
15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013

Best paper award at the 15th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2013)

Available from: 2013-09-27 Created: 2013-09-27 Last updated: 2014-09-12

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Peña, Jose M.
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Database and information techniquesThe Institute of Technology
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