← Back to papers
2014 AISTATS AISTATS 2014

An inclusion optimal algorithm for chain graph structure learning

Abstract

This paper presents and proves an extension of Meek’s conjecture to chain graphs under the Lauritzen-Wermuth-Frydenberg interpretation. The proof of the conjecture leads to the development of a structure learning algorithm that finds an inclusion optimal chain graph for any given probability distribution satisfying the composition property. Finally, the new algorithm is experimentally evaluated.

🌉 Interdisciplinary Bridge — Machine Learning and Mathematics & Optimization
🐣 Hot Topic Early Bird — structure learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Jose Peña , Dag Sonntag , Jens Nielsen

Topics

Machine Learning > Optimization & Theory > Bayesian Inference Mathematics & Optimization > Mathematics > Graph Theory

Keywords

structure learning probabilistic inference bayesian network graphical model chain graph
Download PDF

Related papers

Improved Bounds for Online Learning Over the Permutahedron and Other Ranking Polytopes 2014
PAC-Bayesian Theory for Transductive Learning 2014
Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus 2014
Analytic Long-Term Forecasting with Periodic Gaussian Processes 2014
Exploiting the Limits of Structure Learning via Inherent Symmetry 2014