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2020 PGM PGM 2020

GOBNILP: Learning Bayesian network structure with integer programming

Abstract

The GOBNILP system for learning Bayesian networks is presented. Both the Python and C implementations are discussed. The usefulness of learning multiple BNs is highlighted. Current work on ‘pricing in’ new integer programming variables is presented.

🌉 Interdisciplinary Bridge — Machine Learning and Mathematics & Optimization
🐣 Hot Topic Early Bird — combinatorial optimization
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Reinforcement Learning

Authors

James Cussens

Topics

Machine Learning > Optimization & Theory > Optimization Mathematics & Optimization > Optimization > Combinatorial Optimization Machine Learning > Bayesian & Probabilistic > Bayesian Learning Machine Learning > Core Methods > Graphical Models

Keywords

combinatorial optimization structure learning integer programming bayesian network bayesian network structure learning
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