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A Bayesian approach for the analysis of triadic data in cognitive social structures

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  • Tim B. Swartz
  • Paramjit S. Gill
  • Saman Muthukumarana

Abstract

type="main" xml:id="rssc12096-abs-0001"> The paper proposes a fully Bayesian approach for the analysis of triadic data in social networks. Inference is based on Markov chain Monte Carlo methods as implemented in the software package WinBUGS. We apply the methodology to two data sets to highlight the ease with which cognitive social structures can be analysed.

Suggested Citation

  • Tim B. Swartz & Paramjit S. Gill & Saman Muthukumarana, 2015. "A Bayesian approach for the analysis of triadic data in cognitive social structures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(4), pages 593-610, August.
    • Handle: RePEc:bla:jorssc:v:64:y:2015:i:4:p:593-610
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    File URL: http://hdl.handle.net/10.1111/rssc.2015.64.issue-4
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    Cited by:

    1. Terrence D. Jorgensen & Aditi M. Bhangale & Yves Rosseel, 2024. "Two-Stage Limited-Information Estimation for Structural Equation Models of Round-Robin Variables," Stats, MDPI, vol. 7(1), pages 1-34, February.
    2. Sosa, Juan & Betancourt, Brenda, 2022. "A latent space model for multilayer network data," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).

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