ESTIMATION OF GAUSSIAN DIRECTED ACYCLIC GRAPHS USING PARTIAL ORDERING INFORMATION WITH APPLICATIONS TO DREAM3 NETWORKS AND DAIRY CATTLE DATA

• 成果类型: Article
• 署名作者: Rahman, Syed; Khare, Kshitij; Michailidis, George; Martinez, Carlos; Carulla, Juan
• 署名单位: Apple Inc; State University System of Florida; University of Florida; Corporacion Colombiana de Investigacion Agropecuaria, AGROSAVIA; Universidad Nacional de Colombia
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1636
• 发表日期: 2023
• 页码: 929-960
• 关键词: learning bayesian networks; MODEL; CONVERGENCE; Consistency; likelihood; inference; selection

摘要

Estimating a directed acyclic graph (DAG) from observational data rep-resents a canonical learning problem and has generated a lot of interest in recent years. Research has focused mostly on the following two cases: when no information regarding the ordering of the nodes in the DAG is available and when a domain-specific complete ordering of the nodes is available. In this paper, motivated by a recent application in dairy science, we develop a method for DAG estimation for the middle scenario, where partition-based partial ordering of the nodes is known based on domain-specific knowledge. We develop an efficient algorithm that solves the posited problem, coined Partition-DAG. Through extensive simulations, using the DREAM3 Yeast networks, we illustrate that Partition-DAG effectively incorporates the partial ordering information to improve both speed and accuracy. We then illustrate the usefulness of Partition-DAG by applying it to recently collected dairy cattle data, and inferring relationships between various variables involved in dairy agroecosystems.