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

Author

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  • Yize Zhao

    (Yale University)

  • Zhe Sun

    (Yale University)

  • Jian Kang

    (University of Michigan)

Abstract

This discussion provides comments for the reviewed methods and the potential future directions based on the review paper “Bayesian Graphical Models for Modern Biological Applications”. Simulations are conducted to assess model performance.

Suggested Citation

  • Yize Zhao & Zhe Sun & Jian Kang, 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 279-286, June.
    • Handle: RePEc:spr:stmapp:v:31:y:2022:i:2:d:10.1007_s10260-021-00606-1
    DOI: 10.1007/s10260-021-00606-1
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    References listed on IDEAS

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    1. J. Møller & A. N. Pettitt & R. Reeves & K. K. Berthelsen, 2006. "An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants," Biometrika, Biometrika Trust, vol. 93(2), pages 451-458, June.
    2. Patrick Danaher & Pei Wang & Daniela M. Witten, 2014. "The joint graphical lasso for inverse covariance estimation across multiple classes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(2), pages 373-397, March.
    3. Elin Shaddox & Francesco C. Stingo & Christine B. Peterson & Sean Jacobson & Charmion Cruickshank-Quinn & Katerina Kechris & Russell Bowler & Marina Vannucci, 2018. "A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 59-85, April.
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