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Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo

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  • Michael Schweinberger

    (University of Missouri)

Abstract

It is a pleasure to congratulate Ni et al. (Stat Methods Appl 490:1–32, 2021) on the recent advances in Bayesian graphical models reviewed in Ni et al. (Stat Methods Appl 490:1–32, 2021). The authors have given considerable thought to the construction and estimation of Bayesian graphical models that capture salient features of biological networks. My discussion focuses on computational challenges and opportunities along with priors, pointing out limitations of the Markov random field priors reviewed in Ni et al. (Stat Methods Appl 490:1–32, 2021) and exploring possible generalizations that capture additional features of conditional independence graphs, such as hub structure and clustering. I conclude with a short discussion of the intersection of graphical models and random graph models.

Suggested Citation

  • Michael Schweinberger, 2022. "Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 253-260, June.
    • Handle: RePEc:spr:stmapp:v:31:y:2022:i:2:d:10.1007_s10260-021-00600-7
    DOI: 10.1007/s10260-021-00600-7
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    References listed on IDEAS

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    1. Sundberg,Rolf, 2019. "Statistical Modelling by Exponential Families," Cambridge Books, Cambridge University Press, number 9781108701112.
    2. Steffen Lauritzen & Alessandro Rinaldo & Kayvan Sadeghi, 2018. "Random networks, graphical models and exchangeability," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(3), pages 481-508, June.
    3. Sundberg,Rolf, 2019. "Statistical Modelling by Exponential Families," Cambridge Books, Cambridge University Press, number 9781108476591.
    4. Christine Peterson & Francesco C. Stingo & Marina Vannucci, 2015. "Bayesian Inference of Multiple Gaussian Graphical Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 159-174, March.
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