Learning AMP Chain Graphs under Faithfulness

2012/04/24 by José M. Peña, Peña, Jose M. · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning in Bioinformatics #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1204.5357

openalex publication_date 2012/04/24 · openalex created_date 2019/07/30 · openalex updated_date 2026/07/28

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.

Citations

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

Cited by

• Every LWF and AMP chain graph originates from a set of causal models

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