2013/01/23 by Nir Friedman, Friedman, Nir, Moisés Goldszmidt +5 · 5 citations
Computer Science · Decision Sciences · Mathematics · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Management and Algorithms #Data Quality and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1301.6695
Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999)
arxiv created 2013/01/23 · openalex publication_date 2013/01/23 · arxiv updated 2013/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In recent years there has been significant progress in algorithms and methods for inducing Bayesian networks from data. However, in complex data analysis problems, we need to go beyond being satisfied with inducing networks with high scores. We need to provide confidence measures on features of these networks: Is the existence of an edge between two nodes warranted? Is the Markov blanket of a given node robust? Can we say something about the ordering of the variables? We should be able to address these questions, even when the amount of data is not enough to induce a high scoring network. In this paper we propose Efron's Bootstrap as a computationally efficient approach for answering these questions. In addition, we propose to use these confidence measures to induce better structures from the data, and to detect the presence of latent variables.