Structural learning for Bayesian networks by testing complete separators in prime blocks

Ping Feng Xu, Jianhua Guo*, Man Lai TANG

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)


In this paper, we consider how to recover the structure of a Bayesian network from a moral graph. We present a more accurate characterization of moral edges, based on which a complete subset (i.e., a separator) contained in the neighbor set of one vertex of the putative moral edge in some prime block of the moral graph can be chosen. This results in a set of separators needing to be searched generally smaller than the sets required by some existing algorithms. A so-called structure-finder algorithm is proposed for structural learning. The complexity analysis of the proposed algorithm is discussed and compared with those for several existing algorithms. We also demonstrate how to construct the moral graph locally from, separately, the Markov blanket, domain knowledge and d-separation trees. Simulation studies are used to evaluate the performances of various strategies for structural learning. We also analyze a gene expression data set by using the structure-finder algorithm.

Original languageEnglish
Pages (from-to)3135-3147
Number of pages13
JournalComputational Statistics and Data Analysis
Issue number12
Publication statusPublished - 1 Dec 2011

Scopus Subject Areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Bayesian network
  • Complete separator
  • Conditional independence
  • Moral edge
  • Prime block
  • Structural learning


Dive into the research topics of 'Structural learning for Bayesian networks by testing complete separators in prime blocks'. Together they form a unique fingerprint.

Cite this