BGGM: Bayesian Gaussian Graphical Models in R
Donald R. Williams, Joris Mulder
Abstract
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).
Topics & Concepts
Bayes factorBayesian probabilityBayesian inferencePrior probabilityPosterior probabilityGraphical modelBayes' theoremInferenceSternComputer scienceArtificial intelligenceGaussianBayesian linear regressionMathematicsEngineeringPhysicsQuantum mechanicsMarine engineeringBayesian Modeling and Causal Inference