Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Characterization of essential graphs by means of the operation of legal merging of components
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
A graphical characterization of the largest chain graphs
International Journal of Approximate Reasoning
An alternative Markov property for chain graphs
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Parallell interacting MCMC for learning of topologies of graphical models
Data Mining and Knowledge Discovery
Chain graph interpretations and their relations
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Learning AMP chain graphs and some marginal models thereof under faithfulness
International Journal of Approximate Reasoning
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This paper deals with chain graph models under alternative AMP interpretation. A new representative of an AMP Markov equivalence class, called the largest deflagged graph, is proposed. The representative is based on revealed internal structure of the AMP Markov equivalence class. More specifically, the AMP Markov equivalence class decomposes into finer strong equivalence classes and there exists a distinguished strong equivalence class among those forming the AMP Markov equivalence class. The largest deflagged graph is the largest chain graph in that distinguished strong equivalence class. A composed graphical procedure to get the largest deflagged graph on the basis of any AMP Markov equivalent chain graph is presented. In general, the largest deflagged graph differs from the AMP essential graph, which is another representative of the AMP Markov equivalence class.