Greedy structure learning from data that contain systematic missing values

DOI10.1007/s10994-022-06195-8OpenAlexW4292572097MaRDI QIDQ2102427

Anthony C. Constantinou, Yang Liu

Publication date: 28 November 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2107.04184

zbMATH Keywords

missing datastructure learninginverse probability weightingexpectation-maximisationscore-based learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• bnlearn
• TETRAD

Cites Work

• Unnamed Item
• Unnamed Item
• Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood
• The max-min hill-climbing Bayesian network structure learning algorithm
• Causation, prediction, and search
• Estimating the dimension of a model
• Learning Bayesian networks: The combination of knowledge and statistical data
• Consistent model selection of discrete Bayesian networks from incomplete data
• Learning Bayesian networks from incomplete data with the node-average likelihood
• Inference and missing data
• 10.1162/153244303321897717
• Graphical Models for Processing Missing Data
• A Generalization of Sampling Without Replacement From a Finite Universe

This page was built for publication: Greedy structure learning from data that contain systematic missing values