Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings         Authors   Author's Info   Article details         Search    
On Separation Criterion and Recovery Algorithm for Chain Graphs
Milan Studeny
Abstract:
Chain graphs give a natural unifying point of view on Markov and Bayesian networks and enlarge the potential of graphical models for description of conditional independence structures. In the paper a direct graphical separation criterion for chain graphs, called c-separation, which generalizes the d-separation criterion for Bayesian networks is introduced (recalled). It is equivalent to the classic moralization criterion for chain graphs and complete in sense that for every chain graph there exists a probability distribution satisfying exactly conditional independencies derivable from the chain graph by the c-separation criterion. Every class of Markov equivalent chain graphs can be uniquely described by a natural representative, called the largest chain graph. A recovery algorithm, which on basis of the (conditional) dependency model induced by an unknown chain graph finds the corresponding largest chain graph, is presented.
Keywords: Chain graph, conditional independence, Markov equivalence of chain graphs, c-separat
Pages: 509-516
PS Link: ftp://ftp.utia.cas.cz/pub/staff/studeny/portland.ps.Z
PDF Link: /papers/96/p509-studeny.pdf
BibTex:
@INPROCEEDINGS{Studeny96,
AUTHOR = "Milan Studeny ",
TITLE = "On Separation Criterion and Recovery Algorithm for Chain Graphs",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "509--516"
}


hosted by DSL   •   site info   •   help