IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v81y2011i2p220-230.html
   My bibliography  Save this article

A Gaussian mixed model for learning discrete Bayesian networks

Author

Listed:
  • Balov, Nikolay

Abstract

In this paper, we address the problem of learning discrete Bayesian networks from noisy data. A graphical model based on a mixture of Gaussian distributions with categorical mixing structure coming from a discrete Bayesian network is considered. The network learning is formulated as a maximum likelihood estimation problem and performed by employing an EM algorithm. The proposed approach is relevant to a variety of statistical problems for which Bayesian network models are suitable--from simple regression analysis to learning gene/protein regulatory networks from microarray data.

Suggested Citation

  • Balov, Nikolay, 2011. "A Gaussian mixed model for learning discrete Bayesian networks," Statistics & Probability Letters, Elsevier, vol. 81(2), pages 220-230, February.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:2:p:220-230
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(10)00328-7
    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. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
    2. Salzman Peter & Almudevar Anthony, 2006. "Using Complexity for the Estimation of Bayesian Networks," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-23, August.
    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. Kuroda, Masahiro & Sakakihara, Michio & Geng, Zhi, 2008. "Acceleration of the EM and ECM algorithms using the Aitken [delta]2 method for log-linear models with partially classified data," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2332-2338, October.
    2. Chen, Yen-Liang & Hu, Hui-Ling, 2006. "An overlapping cluster algorithm to provide non-exhaustive clustering," European Journal of Operational Research, Elsevier, vol. 173(3), pages 762-780, September.
    3. Croft, J. & Smith, J. Q., 2003. "Discrete mixtures in Bayesian networks with hidden variables: a latent time budget example," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 539-547, January.
    4. Fazia Abdat & Sylvie Leclercq & Xavier Cuny & Claire Tissot, 2014. "Extracting recurrent scenarios from narrative texts using a Bayesian network: Application to serious occupational accidents with movement disturbance," Post-Print hal-01578382, HAL.
    5. Esma Nur Cinicioglu & Gül Huyugüzel Kışla & A. Özlem Önder & Y. Gülnur Muradoğlu, 2024. "The Changing Behavior of the European Credit Default Swap Spreads During the Covid-19 Pandemic: A Bayesian Network Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1213-1254, March.
    6. Hongyu Wang & Jian Tang & Pengpeng Xu & Rundong Chen & Haona Yao, 2022. "Research on the Influence Mechanism of Street Vitality in Mountainous Cities Based on a Bayesian Network: A Case Study of the Main Urban Area of Chongqing," Land, MDPI, vol. 11(5), pages 1-22, May.
    7. Minjeong Jeon & Frank Rijmen & Sophia Rabe-Hesketh, 2013. "Modeling Differential Item Functioning Using a Generalization of the Multiple-Group Bifactor Model," Journal of Educational and Behavioral Statistics, , vol. 38(1), pages 32-60, February.
    8. Claudia Tarantola & Paola Vicard & Ioannis Ntzoufras, 2012. "Monitoring and Improving Greek Banking Services Using Bayesian Networks: an Analysis of Mystery Shopping Data," Quaderni di Dipartimento 160, University of Pavia, Department of Economics and Quantitative Methods.
    9. Silvia Salini & Ron Kenett, 2009. "Bayesian networks of customer satisfaction survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1177-1189.
    10. Sheehan, Barry & Murphy, Finbarr & Mullins, Martin & Ryan, Cian, 2019. "Connected and autonomous vehicles: A cyber-risk classification framework," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 523-536.
    11. Astrid Kemperman & Pauline van den Berg & Minou Weijs-Perrée & Kevin Uijtdewillegen, 2019. "Loneliness of Older Adults: Social Network and the Living Environment," IJERPH, MDPI, vol. 16(3), pages 1-16, January.
    12. Jie Fan & Baoyin Liu & Xiaodong Ming & Yong Sun & Lianjie Qin, 2022. "The amplification effect of unreasonable human behaviours on natural disasters," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-10, December.
    13. Garvey, Myles D. & Carnovale, Steven & Yeniyurt, Sengun, 2015. "An analytical framework for supply network risk propagation: A Bayesian network approach," European Journal of Operational Research, Elsevier, vol. 243(2), pages 618-627.
    14. Kim, Seong-Ho & Kim, Sung-Ho, 2006. "A divide-and-conquer approach in applying EM for large recursive models with incomplete categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 611-641, February.
    15. Rui Han & Shiqi Yang, 2023. "A Study on Industrial Heritage Renewal Strategy Based on Hybrid Bayesian Network," Sustainability, MDPI, vol. 15(13), pages 1-32, July.
    16. Svend Rasmussen & Anders L. Madsen & Mogens Lund, 2013. "Bayesian network as a modelling tool for risk management in agriculture," IFRO Working Paper 2013/12, University of Copenhagen, Department of Food and Resource Economics.
    17. Dinis, D. & Teixeira, A.P. & Guedes Soares, C., 2020. "Probabilistic approach for characterising the static risk of ships using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    18. Dinis, Duarte & Barbosa-Póvoa, Ana & Teixeira, Ângelo Palos, 2022. "Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems," International Journal of Forecasting, Elsevier, vol. 38(1), pages 178-192.
    19. Janssens, Davy & Wets, Geert & Brijs, Tom & Vanhoof, Koen & Arentze, Theo & Timmermans, Harry, 2006. "Integrating Bayesian networks and decision trees in a sequential rule-based transportation model," European Journal of Operational Research, Elsevier, vol. 175(1), pages 16-34, November.
    20. Frank Rijmen & Minjeong Jeon & Matthias von Davier & Sophia Rabe-Hesketh, 2014. "A Third-Order Item Response Theory Model for Modeling the Effects of Domains and Subdomains in Large-Scale Educational Assessment Surveys," Journal of Educational and Behavioral Statistics, , vol. 39(4), pages 235-256, August.

    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:stapro:v:81:y:2011:i:2:p:220-230. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.