Minimax posterior convergence rates and model selection consistency in high-dimensional DAG models based on sparse Cholesky factors
稀疏 Cholesky 因子高维 DAG 模型的极小极大后验收敛率与选择一致性
In this paper we study the high-dimensional sparse directed acyclic graph (DAG) models under the empirical sparse Cholesky prior. Among our results, strong model selection consistency or graph selection consistency is obtained under more general conditions than those in the existing literature. Compared to Cao, Khare and Ghosh [Ann. Statist. (2019) 47 319–348], the required conditions are weakened in terms of the dimensionality, sparsity and lower bound of the nonzero…