Learning AMP Chain Graphs under Faithfulness

Abstract

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.

References in corpus (3)

• Causal Inference and Causal Explanation with Background Knowledge
• Finding Optimal Bayesian Networks
• On Local Optima in Learning Bayesian Networks

Cited by in corpus (1)

• Identifiability of AMP chain graph models