Bayesian inference of clustering and multiple Gaussian graphical models selection

2021 ◽  
Author(s):  
Wei Dai ◽  
Baisuo Jin
Keyword(s):  
Bayesian Inference ◽  
Graphical Models ◽  
Gaussian Graphical Models

scholarly journals Fast Bayesian inference in large Gaussian graphical models

Biometrics ◽  
2019 ◽  
Vol 75 (4) ◽  
pp. 1288-1298
Author(s):  
Gwenaël G. R. Leday ◽  
Sylvia Richardson
Keyword(s):  
Bayesian Inference ◽  
Graphical Models ◽  
Gaussian Graphical Models

scholarly journals Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks

2017 ◽  
Vol 11 (4) ◽  
pp. 2222-2251 ◽  
Author(s):  
Linda S. L. Tan ◽  
Ajay Jasra ◽  
Maria De Iorio ◽  
Timothy M. D. Ebbels
Keyword(s):  
Bayesian Inference ◽  
Graphical Models ◽  
Gaussian Graphical Models

scholarly journals Bayesian Inference of Multiple Gaussian Graphical Models

2015 ◽  
Vol 110 (509) ◽  
pp. 159-174 ◽  
Author(s):  
Christine Peterson ◽  
Francesco C. Stingo ◽  
Marina Vannucci
Keyword(s):  
Bayesian Inference ◽  
Graphical Models ◽  
Gaussian Graphical Models

scholarly journals BGGM: Bayesian Gaussian Graphical Models in R

2020 ◽  
Author(s):  
Donald Ray Williams ◽  
Joris Mulder
Keyword(s):  
Bayesian Inference ◽  
Hypothesis Testing ◽  
Graphical Models ◽  
Model Comparison ◽  
Bayes Factor ◽  
R Package ◽  
Predictive Distribution ◽  
Gaussian Graphical Models ◽  
Posterior Predictive Distribution

The R package BGGM provides tools for making Bayesian inference in Gaussian graphicalmodels (GGM). The methods are organized around two general approaches for Bayesian inference: (1) estimation and (2) hypothesis testing. The key distinction is that the formerfocuses on either the posterior or posterior predictive distribution (Gelman, Meng, & Stern,1996; see section 5 in Rubin, 1984), whereas the latter focuses on model comparison withthe Bayes factor (Jeffreys, 1961; Kass & Raftery, 1995).

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