46 citations · 294 across the 26 of their papers we have counts for
6 papers · 1 filter
Partitioned hybrid learning of Bayesian network structures
Jireh Huang, Qing Zhou
We develop a novel hybrid method for Bayesian network structure learning called partitioned hybrid greedy search (pHGS), composed of three distinct yet compatible new algorithms: P…
Causal network learning with non-invertible functional relationships
Bingling Wang, Qing Zhou
Discovery of causal relationships from observational data is an important problem in many areas. Several recent results have established the identifiability of causal DAGs with non…
Learning Gaussian DAGs from Network Data
Hangjian Li, Oscar Hernan Madrid Padilla, Qing Zhou
Structural learning of directed acyclic graphs (DAGs) or Bayesian networks has been studied extensively under the assumption that data are independent. We propose a new Gaussian DA…
Optimizing regularized Cholesky score for order-based learning of Bayesian networks
Qiaoling Ye, Arash A. Amini, Qing Zhou
Bayesian networks are a class of popular graphical models that encode causal and conditional independence relations among variables by directed acyclic graphs (DAGs). We propose a…
Learning big Gaussian Bayesian networks: partition, estimation, and fusion
Jiaying Gu, Qing Zhou
Structure learning of Bayesian networks has always been a challenging problem. Nowadays, massive-size networks with thousands or more of nodes but fewer samples frequently appear i…
Learning Large-Scale Bayesian Networks with the sparsebn Package
Bryon Aragam, Jiaying Gu, Qing Zhou
Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands-…