Penalized estimation of directed acyclic graphs from discrete data

• Published in: Statistics and computing Vol. 29; no. 1; pp. 161 - 176
• Main Authors: Gu, Jiaying, Fu, Fei, Zhou, Qing
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2019; Springer Nature B.V
• Subjects: Artificial Intelligence; Bayesian analysis; Computer simulation; Graph theory; Machine learning; Mathematics and Statistics; Parents; Probability and Statistics in Computer Science; Statistical Theory and Methods; Statistics; Statistics and Computing/Statistics Programs; Structure learning; Group norm penalty; Coordinate descent; Discrete Bayesian network; Multi-logit regression
• ISSN: 0960-3174; 1573-1375
• DOI: 10.1007/s11222-018-9801-y

Bayesian networks, with structure given by a directed acyclic graph (DAG), are a popular class of graphical models. However, learning Bayesian networks from discrete or categorical data is particularly challenging, due to the large parameter space and the difficulty in searching for a sparse structure. In this article, we develop a maximum penalized likelihood method to tackle this problem. Instead of the commonly used multinomial distribution, we model the conditional distribution of a node given its parents by multi-logit regression, in which an edge is parameterized by a set of coefficient vectors with dummy variables encoding the levels of a node. To obtain a sparse DAG, a group norm penalty is employed, and a blockwise coordinate descent algorithm is developed to maximize the penalized likelihood subject to the acyclicity constraint of a DAG. When interventional data are available, our method constructs a causal network, in which a directed edge represents a causal relation. We apply our method to various simulated and real data sets. The results show that our method is very competitive, compared to many existing methods, in DAG estimation from both interventional and high-dimensional observational data.