IDEAS home Printed from https://ideas.repec.org/a/eee/thpobi/v81y2012i2p131-143.html
   My bibliography  Save this article

On the choice of prior density for the Bayesian analysis of pedigree structure

Author

Listed:
  • Almudevar, Anthony
  • LaCombe, Jason

Abstract

This article is concerned with the choice of structural prior density for use in a fully Bayesian approach to pedigree inference. It is found that the choice of prior has considerable influence on the accuracy of the estimation. To guide this choice, a scale invariance property is introduced. Under a structural prior with this property, the marginal prior distribution of the local properties of a pedigree node (number of parents, offspring, etc.) does not depend on the number of nodes in the pedigree. Such priors are found to arise naturally by an application of the Minimum Description Length (MDL) principle, under which construction of a prior becomes equivalent to the problem of determining the length of a code required to encode a pedigree, using the principles of information theory. The approach is demonstrated using simulated and actual data, and is compared to two well-known applications, CERVUS and COLONY.

Suggested Citation

  • Almudevar, Anthony & LaCombe, Jason, 2012. "On the choice of prior density for the Bayesian analysis of pedigree structure," Theoretical Population Biology, Elsevier, vol. 81(2), pages 131-143.
    • Handle: RePEc:eee:thpobi:v:81:y:2012:i:2:p:131-143
    DOI: 10.1016/j.tpb.2011.12.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040580911001067
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tpb.2011.12.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Cowell, Robert G., 2009. "Efficient maximum likelihood pedigree reconstruction," Theoretical Population Biology, Elsevier, vol. 76(4), pages 285-291.
    2. Paul Sheridan & Takeshi Kamimura & Hidetoshi Shimodaira, 2010. "A Scale-Free Structure Prior for Graphical Models with Applications in Functional Genomics," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-12, November.
    3. Ellis, Byron & Wong, Wing Hung, 2008. "Learning Causal Bayesian Network Structures From Experimental Data," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 778-789, June.
    4. Riester, Markus & Stadler, Peter F. & Klemm, Konstantin, 2010. "Reconstruction of pedigrees in clonal plant populations," Theoretical Population Biology, Elsevier, vol. 78(2), pages 109-117.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anderson, Eric C. & Ng, Thomas C., 2016. "Bayesian pedigree inference with small numbers of single nucleotide polymorphisms via a factor-graph representation," Theoretical Population Biology, Elsevier, vol. 107(C), pages 39-51.
    2. Anderson Eric C., 2012. "Large-scale Parentage Inference with SNPs: an Efficient Algorithm for Statistical Confidence of Parent Pair Allocations," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(5), pages 1-28, November.
    3. Cowell, Robert G., 2013. "A simple greedy algorithm for reconstructing pedigrees," Theoretical Population Biology, Elsevier, vol. 83(C), pages 55-63.
    4. Almudevar, Anthony, 2016. "An information theoretic approach to pedigree reconstruction," Theoretical Population Biology, Elsevier, vol. 107(C), pages 52-64.

    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. Almudevar, Anthony, 2016. "An information theoretic approach to pedigree reconstruction," Theoretical Population Biology, Elsevier, vol. 107(C), pages 52-64.
    2. Cowell, Robert G., 2013. "A simple greedy algorithm for reconstructing pedigrees," Theoretical Population Biology, Elsevier, vol. 83(C), pages 55-63.
    3. Davide Altomare & Guido Consonni & Luca La Rocca, 2013. "Objective Bayesian Search of Gaussian Directed Acyclic Graphical Models for Ordered Variables with Non-Local Priors," Biometrics, The International Biometric Society, vol. 69(2), pages 478-487, June.
    4. Federico Castelletti & Alessandro Mascaro, 2021. "Structural learning and estimation of joint causal effects among network-dependent variables," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1289-1314, December.
    5. Sun, M. & Jobling, M.A. & Taliun, D. & Pramstaller, P.P. & Egeland, T. & Sheehan, N.A., 2016. "On the use of dense SNP marker data for the identification of distant relative pairs," Theoretical Population Biology, Elsevier, vol. 107(C), pages 14-25.
    6. Riester, Markus & Stadler, Peter F. & Klemm, Konstantin, 2010. "Reconstruction of pedigrees in clonal plant populations," Theoretical Population Biology, Elsevier, vol. 78(2), pages 109-117.
    7. Yan Zhou & Peter X.‐K. Song & Xiaoquan Wen, 2021. "Structural factor equation models for causal network construction via directed acyclic mixed graphs," Biometrics, The International Biometric Society, vol. 77(2), pages 573-586, June.
    8. Sheehan, Nuala A. & Bartlett, Mark & Cussens, James, 2014. "Improved maximum likelihood reconstruction of complex multi-generational pedigrees," Theoretical Population Biology, Elsevier, vol. 97(C), pages 11-19.
    9. Gurami Tsitsiashvili & Victor Bulgakov, 2021. "New Applied Problems in the Theory of Acyclic Digraphs," Mathematics, MDPI, vol. 10(1), pages 1-10, December.
    10. Sang Wan Lee & John P O’Doherty & Shinsuke Shimojo, 2015. "Neural Computations Mediating One-Shot Learning in the Human Brain," PLOS Biology, Public Library of Science, vol. 13(4), pages 1-36, April.
    11. Anderson, Eric C. & Ng, Thomas C., 2016. "Bayesian pedigree inference with small numbers of single nucleotide polymorphisms via a factor-graph representation," Theoretical Population Biology, Elsevier, vol. 107(C), pages 39-51.
    12. Sheridan, Paul & Yagahara, Yuichi & Shimodaira, Hidetoshi, 2012. "Measuring preferential attachment in growing networks with missing-timelines using Markov chain Monte Carlo," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 5031-5040.
    13. Zhang, Hongmei & Huang, Xianzheng & Han, Shengtong & Rezwan, Faisal I. & Karmaus, Wilfried & Arshad, Hasan & Holloway, John W., 2021. "Gaussian Bayesian network comparisons with graph ordering unknown," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    14. Liang, Faming & Zhang, Jian, 2009. "Learning Bayesian networks for discrete data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 865-876, February.
    15. Huang, Xianzheng & Zhang, Hongmei, 2021. "Tests for differential Gaussian Bayesian networks based on quadratic inference functions," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    16. Louis Anthony (Tony) Cox, Jr., 2009. "Why Reduced‐Form Regression Models of Health Effects Versus Exposures Should Not Replace QRA: Livestock Production and Infant Mortality as an Example," Risk Analysis, John Wiley & Sons, vol. 29(12), pages 1664-1671, December.

    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:thpobi:v:81:y:2012:i:2:p:131-143. 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: https://www.journals.elsevier.com/intelligence .

    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.