Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs
2015 (English)In: SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING WITH UNCERTAINTY, ECSQARU 2015, SPRINGER-VERLAG BERLIN , 2015, Vol. 9161, 335-345 p.Conference paper (Refereed)Text
We address some computational issues that may hinder the use of AMP chain graphs in practice. Specifically, we show how a discrete probability distribution that satisfies all the independencies represented by an AMP chain graph factorizes according to it. We show how this factorization makes it possible to perform inference and parameter learning efficiently, by adapting existing algorithms for Markov and Bayesian networks. Finally, we turn our attention to another issue that may hinder the use of AMP CGs, namely the lack of an intuitive interpretation of their edges. We provide one such interpretation.
Place, publisher, year, edition, pages
SPRINGER-VERLAG BERLIN , 2015. Vol. 9161, 335-345 p.
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 9161
Computer and Information Science
IdentifiersURN: urn:nbn:se:liu:diva-123538DOI: 10.1007/978-3-319-20807-7_30ISI: 000364847800030ISBN: 978-3-319-20807-7; 978-3-319-20806-0OAI: oai:DiVA.org:liu-123538DiVA: diva2:886129
13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU)