Approximate structure learning for large Bayesian networks

We present approximate structure learning algorithms for Bayesian networks. We discuss the two main phases of the task: the preparation of the cache of the scores and structure optimization, both with bounded and unbounded treewidth. We improve on state-of-the-art methods that rely on an ordering-ba...

Full description

Saved in:
Bibliographic Details
Published in:Machine learning 2018-09, Vol.107 (8-10), p.1209-1227
Main Authors: Scanagatta, Mauro, Corani, Giorgio, de Campos, Cassio Polpo, Zaffalon, Marco
Format: Article
Language:English
Subjects:
Artificial Intelligence
Bayesian analysis
Computer Science
Control
Machine learning
Mechatronics
Natural Language Processing (NLP)
Networks
Robotics
Run time (computers)
Simulation and Modeling
Special Issue of the ECML PKDD 2018 Journal Track
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present approximate structure learning algorithms for Bayesian networks. We discuss the two main phases of the task: the preparation of the cache of the scores and structure optimization, both with bounded and unbounded treewidth. We improve on state-of-the-art methods that rely on an ordering-based search by sampling more effectively the space of the orders. This allows for a remarkable improvement in learning Bayesian networks from thousands of variables. We also present a thorough study of the accuracy and the running time of inference, comparing bounded-treewidth and unbounded-treewidth models.
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-018-5701-9