IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v34y2019i3d10.1007_s00180-019-00880-4.html
   My bibliography  Save this article

A note on parallel sampling in Markov graphs

Author

Listed:
  • Verena Bauer

    (Ludwig-Maximilians-Universität München)

  • Karl Fürlinger

    (Munich Network Management Team, Ludwig-Maximilians-Universität München)

  • Göran Kauermann

    (Ludwig-Maximilians-Universität München)

Abstract

The paper proposes the use of parallel computing for Markov graphs as a subclass of exponential random graph models where the network statistics induce a conditional independence structure amongst the edges of the network. This conditional independence allows simulation of edges in parallel using multiple computing cores. Simulation in Markov models is helpful, since parameter estimation cannot be carried out analytically but requires simulation-based routines such as Markov chain Monte Carlo. In particular in large networks this can be computationally very demanding or even infeasible. Therefore, numerical enhancements are useful to accelerate computation.

Suggested Citation

  • Verena Bauer & Karl Fürlinger & Göran Kauermann, 2019. "A note on parallel sampling in Markov graphs," Computational Statistics, Springer, vol. 34(3), pages 1087-1107, September.
  • Handle: RePEc:spr:compst:v:34:y:2019:i:3:d:10.1007_s00180-019-00880-4
    DOI: 10.1007/s00180-019-00880-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-019-00880-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00180-019-00880-4?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. Morris, Martina & Handcock, Mark S. & Hunter, David R., 2008. "Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i04).
    2. Johan Koskinen & Peng Wang & Garry Robins & Philippa Pattison, 2018. "Outliers and Influential Observations in Exponential Random Graph Models," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 809-830, December.
    3. Eddelbuettel, Dirk & Francois, Romain, 2011. "Rcpp: Seamless R and C++ Integration," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i08).
    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. Wilson J. Wright & Peter N. Neitlich & Alyssa E. Shiel & Mevin B. Hooten, 2022. "Mechanistic spatial models for heavy metal pollution," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
    2. Alex Stivala & Peng Wang & Alessandro Lomi, 2024. "ALAAMEE: Open-source software for fitting autologistic actor attribute models," PLOS Complex Systems, Public Library of Science, vol. 1(4), pages 1-32, December.
    3. repec:plo:pcbi00:1005544 is not listed on IDEAS
    4. Shen, Yunyi & Olson, Erik R. & Van Deelen, Timothy R., 2021. "Spatially explicit modeling of community occupancy using Markov Random Field models with imperfect observation: Mesocarnivores in Apostle Islands National Lakeshore," Ecological Modelling, Elsevier, vol. 459(C).
    5. Javier Sánchez García & Salvador Cruz Rambaud, 2024. "The network econometrics of financial concentration," Review of Managerial Science, Springer, vol. 18(7), pages 2007-2045, July.
    6. Fernández de Marcos Giménez de los Galanes, Alberto & García Portugués, Eduardo, 2022. "Data-driven stabilizations of goodness-of-fit tests," DES - Working Papers. Statistics and Econometrics. WS 35324, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Bandara, Kanchana & Varpe, Øystein & Ji, Rubao & Eiane, Ketil, 2018. "A high-resolution modeling study on diel and seasonal vertical migrations of high-latitude copepods," Ecological Modelling, Elsevier, vol. 368(C), pages 357-376.
    8. Carter Allen & Yuzhou Chang & Brian Neelon & Won Chang & Hang J. Kim & Zihai Li & Qin Ma & Dongjun Chung, 2023. "A Bayesian multivariate mixture model for high throughput spatial transcriptomics," Biometrics, The International Biometric Society, vol. 79(3), pages 1775-1787, September.
    9. Sloot Henrik, 2022. "Implementing Markovian models for extendible Marshall–Olkin distributions," Dependence Modeling, De Gruyter, vol. 10(1), pages 308-343, January.
    10. Cindy Frascolla & Guillaume Lecuelle & Pascal Schlich & Hervé Cardot, 2022. "Two sample tests for Semi-Markov processes with parametric sojourn time distributions: an application in sensory analysis," Computational Statistics, Springer, vol. 37(5), pages 2553-2580, November.
    11. Juan C. Laria & M. Carmen Aguilera-Morillo & Rosa E. Lillo, 2023. "Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models," Statistical Papers, Springer, vol. 64(1), pages 227-253, February.
    12. Ghislain Geniaux, 2024. "Speeding up estimation of spatially varying coefficients models," Journal of Geographical Systems, Springer, vol. 26(3), pages 293-327, July.
    13. Yaveroğlu, Ömer Nebil & Fitzhugh, Sean M. & Kurant, Maciej & Markopoulou, Athina & Butts, Carter T. & Pržulj, Nataša, 2015. "ergm.graphlets: A Package for ERG Modeling Based on Graphlet Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i12).
    14. Hess, Stephane & Palma, David, 2019. "Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    15. Samson, Adeline & Tamborrino, Massimiliano & Tubikanec, Irene, 2025. "Inference for the stochastic FitzHugh-Nagumo model from real action potential data via approximate Bayesian computation," Computational Statistics & Data Analysis, Elsevier, vol. 204(C).
    16. Samrachana Adhikari & Tracy Sweet & Brian Junker, 2021. "Analysis of longitudinal advice‐seeking networks following implementation of high stakes testing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1475-1500, October.
    17. François Bachoc & Marc G Genton & Klaus Nordhausen & Anne Ruiz-Gazen & Joni Virta, 2020. "Spatial blind source separation," Biometrika, Biometrika Trust, vol. 107(3), pages 627-646.
    18. Bill Venables, 2017. "JOHN M. CHAMBERS . Extending R . Boca Raton : CRC Press," Biometrics, The International Biometric Society, vol. 73(2), pages 709-710, June.
    19. Di Mari, Roberto & Bakk, Zsuzsa & Oser, Jennifer & Kuha, Jouni, 2023. "A two-step estimator for multilevel latent class analysis with covariates," LSE Research Online Documents on Economics 119994, London School of Economics and Political Science, LSE Library.
    20. Xiaotian Zhu & David R. Hunter, 2019. "Clustering via finite nonparametric ICA mixture models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 65-87, March.
    21. Roberto Mari & Zsuzsa Bakk & Jennifer Oser & Jouni Kuha, 2023. "A two-step estimator for multilevel latent class analysis with covariates," Psychometrika, Springer;The Psychometric Society, vol. 88(4), pages 1144-1170, 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:spr:compst:v:34:y:2019:i:3:d:10.1007_s00180-019-00880-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.