IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v55y2011i12p3135-3147.html
   My bibliography  Save this article

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

Author

Listed:
  • Xu, Ping-Feng
  • Guo, Jianhua
  • Tang, Man-Lai

Abstract

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.

Suggested Citation

  • Xu, Ping-Feng & Guo, Jianhua & Tang, Man-Lai, 2011. "Structural learning for Bayesian networks by testing complete separators in prime blocks," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3135-3147, December.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:12:p:3135-3147
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947311002209
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Abramson, Bruce & Brown, John & Edwards, Ward & Murphy, Allan & Winkler, Robert L., 1996. "Hailfinder: A Bayesian system for forecasting severe weather," International Journal of Forecasting, Elsevier, vol. 12(1), pages 57-71, March.
    2. Geng, Zhi & Wang, Chi & Zhao, Qiang, 2005. "Decomposition of search for v-structures in DAGs," Journal of Multivariate Analysis, Elsevier, vol. 96(2), pages 282-294, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Regnier, Eva, 2008. "Doing something about the weather," Omega, Elsevier, vol. 36(1), pages 22-32, February.
    2. Robert T. Clemen & Canan Ulu, 2008. "Interior Additivity and Subjective Probability Assessment of Continuous Variables," Management Science, INFORMS, vol. 54(4), pages 835-851, April.
    3. Kazim Topuz & Hasmet Uner & Asil Oztekin & Mehmet Bayram Yildirim, 2018. "Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network," Annals of Operations Research, Springer, vol. 263(1), pages 479-499, April.
    4. Wang, Changzhang & Zhou, You & Zhao, Qiang & Geng, Zhi, 2014. "Discovering and orienting the edges connected to a target variable in a DAG via a sequential local learning approach," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 252-266.
    5. Langseth, Helge & Portinale, Luigi, 2007. "Bayesian networks in reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 92-108.
    6. Hao Zuo & Jinshen Jiang & Yun Zhou, 2024. "DAGOR: Learning DAGs via Topological Sorts and QR Factorization," Mathematics, MDPI, vol. 12(8), pages 1-16, April.
    7. Mark Borsuk & Robert Clemen & Lynn Maguire & Kenneth Reckhow, 2001. "Stakeholder Values and Scientific Modeling in the Neuse River Watershed," Group Decision and Negotiation, Springer, vol. 10(4), pages 355-373, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:55:y:2011:i:12:p:3135-3147. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.