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Bayesian Inference of Multiple Gaussian Graphical Models

Citations

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Cited by:

  1. Huang, Xianzheng & Zhang, Hongmei, 2021. "Tests for differential Gaussian Bayesian networks based on quadratic inference functions," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
  2. Castelletti, Federico & Peluso, Stefano, 2021. "Equivalence class selection of categorical graphical models," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
  3. Federico Castelletti & Guido Consonni, 2020. "Discovering causal structures in Bayesian Gaussian directed acyclic graph models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1727-1745, October.
  4. Paci, Lucia & Consonni, Guido, 2020. "Structural learning of contemporaneous dependencies in graphical VAR models," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
  5. Yang Ni & Francesco C. Stingo & Veerabhadran Baladandayuthapani, 2015. "Bayesian nonlinear model selection for gene regulatory networks," Biometrics, The International Biometric Society, vol. 71(3), pages 585-595, September.
  6. Atchadé, Yves F., 2019. "Quasi-Bayesian estimation of large Gaussian graphical models," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 656-671.
  7. 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.
  8. Yang Ni & Veerabhadran Baladandayuthapani & Marina Vannucci & Francesco C. Stingo, 2022. "Rejoinder to the discussion of “Bayesian graphical models for modern biological applications”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 287-294, June.
  9. Zhixiang Lin & Tao Wang & Can Yang & Hongyu Zhao, 2017. "On joint estimation of Gaussian graphical models for spatial and temporal data," Biometrics, The International Biometric Society, vol. 73(3), pages 769-779, September.
  10. Mehran Aflakparast & Mathisca de Gunst & Wessel van Wieringen, 2020. "Analysis of Twitter data with the Bayesian fused graphical lasso," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-28, July.
  11. Yang Ni & Veerabhadran Baladandayuthapani & Marina Vannucci & Francesco C. Stingo, 2022. "Bayesian graphical models for modern biological applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 197-225, June.
  12. Wessel N. van Wieringen & Carel F. W. Peeters & Renee X. de Menezes & Mark A. van de Wiel, 2018. "Testing for pathway (in)activation by using Gaussian graphical models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1419-1436, November.
  13. Thierry Chekouo & Francesco C. Stingo & James D. Doecke & Kim-Anh Do, 2017. "A Bayesian integrative approach for multi-platform genomic data: A kidney cancer case study," Biometrics, The International Biometric Society, vol. 73(2), pages 615-624, June.
  14. Federico Castelletti, 2020. "Bayesian Model Selection of Gaussian Directed Acyclic Graph Structures," International Statistical Review, International Statistical Institute, vol. 88(3), pages 752-775, December.
  15. Codazzi, Laura & Colombi, Alessandro & Gianella, Matteo & Argiento, Raffaele & Paci, Lucia & Pini, Alessia, 2022. "Gaussian graphical modeling for spectrometric data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
  16. Anindya Bhadra, 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 235-239, June.
  17. Lin Zhang & Andrew DiLernia & Karina Quevedo & Jazmin Camchong & Kelvin Lim & Wei Pan, 2021. "A random covariance model for bi‐level graphical modeling with application to resting‐state fMRI data," Biometrics, The International Biometric Society, vol. 77(4), pages 1385-1396, December.
  18. Naveen Naidu Narisetty, 2020. "Discussion," International Statistical Review, International Statistical Institute, vol. 88(2), pages 330-334, August.
  19. Suprateek Kundu & Benjamin B. Risk, 2021. "Scalable Bayesian matrix normal graphical models for brain functional networks," Biometrics, The International Biometric Society, vol. 77(2), pages 439-450, June.
  20. van Wieringen, Wessel N. & Stam, Koen A. & Peeters, Carel F.W. & van de Wiel, Mark A., 2020. "Updating of the Gaussian graphical model through targeted penalized estimation," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
  21. 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.
  22. Yang Ni & Peter Müller & Yitan Zhu & Yuan Ji, 2018. "Heterogeneous reciprocal graphical models," Biometrics, The International Biometric Society, vol. 74(2), pages 606-615, June.
  23. Marco Molinari & Andrea Cremaschi & Maria De Iorio & Nishi Chaturvedi & Alun D. Hughes & Therese Tillin, 2022. "Bayesian nonparametric modelling of multiple graphs with an application to ethnic metabolic differences," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1181-1204, November.
  24. Christine B. Peterson & Nathan Osborne & Francesco C. Stingo & Pierrick Bourgeat & James D. Doecke & Marina Vannucci, 2020. "Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease," Biometrics, The International Biometric Society, vol. 76(4), pages 1120-1132, December.
  25. Nikolaos Petrakis & Stefano Peluso & Dimitris Fouskakis & Guido Consonni, 2020. "Objective methods for graphical structural learning," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 420-438, August.
  26. Beatrice Franzolini & Alexandros Beskos & Maria De Iorio & Warrick Poklewski Koziell & Karolina Grzeszkiewicz, 2022. "Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the US stock market," Papers 2208.00952, arXiv.org, revised May 2023.
  27. Federico Castelletti & Guido Consonni, 2021. "Bayesian inference of causal effects from observational data in Gaussian graphical models," Biometrics, The International Biometric Society, vol. 77(1), pages 136-149, March.
  28. Lee, Kyoungjae & Cao, Xuan, 2022. "Bayesian joint inference for multiple directed acyclic graphs," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
  29. Jarod Smith & Mohammad Arashi & Andriëtte Bekker, 2022. "Empowering differential networks using Bayesian analysis," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-19, January.
  30. Junghi Kim & Kim‐Anh Do & Min Jin Ha & Christine B. Peterson, 2019. "Bayesian inference of hub nodes across multiple networks," Biometrics, The International Biometric Society, vol. 75(1), pages 172-182, March.
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