Learning AMP Chain Graphs under Faithfulness
This work addresses a theoretical limitation in graphical models for researchers in machine learning and statistics, but it is incremental as it extends existing concepts to a specific interpretation.
The paper tackled the problem of learning AMP chain graphs under faithfulness by presenting a constraint-based algorithm and disproving the extension of Meek's conjecture to these graphs, which shows that efficient score+search algorithms cannot be developed under weaker assumptions.
This paper deals with chain graphs under the alternative Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a constraint based algorithm for learning an AMP chain graph a given probability distribution is faithful to. We also show that the extension of Meek's conjecture to AMP chain graphs does not hold, which compromises the development of efficient and correct score+search learning algorithms under assumptions weaker than faithfulness.