Overview
In this lecture we look at desirable features that priors for Bayesian model averaging or varialbe selction which leads us to mixtures of g-priors.
Readings
Readings: Christensen Chapter 15 and Hoff Chapter 9
Review papers:
Mixtures of g-priors for Bayesian Variable Selection Liang et al (2008) Journal of the American Statistical Association
Criteria for Bayesian model choice with application to variable selection Bayarri et al (2012) Annals of Statistics
To resolve problems with the choice of $g$ we turn to mixtures of $g$ priors and show how they have desirable properties for BMA/BVS.