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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
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    References listed on IDEAS

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    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).
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