MCMC in BMA/BVS and Inference
Abstract
In this lecture show how MCMC can be used for BMA/BVS and challenges within. Using the output we discuss variaous estimators for summarising posteriors.
Readings: Christensen Chapter 15 and Hoff Chapter 9
Mixtures of g-priors for Bayesian Variable Selection Liang et al (2008) Journal of the American Statistical Association
Bayesian Adaptive Sampling for Variable Selection and Model Averaging Clyde, Ghosh, and Littman (2010) Journal of Computational Graphics and Statistics
In this lecture show how MCMC can be used for BMA/BVS and challenges within. Using the output we discuss variaous estimators for summarising posteriors. We illustrate with the diabetes data.
