Model Choice
Abstract
In this lecture we look at model selection from a Bayesian perspective.
Readings: Christensen Chapter 15 and Hoff Chapter 9
Review papers: Bayesian Model Averaging Hoeting et al (1999) Statistical Science
Model Uncertainty Clyde & George (2004) Statistical Science
In this lecture we look at model choice from a Bayesian perspective. We augment the likelihood and prior on parameters in the linear model using indicator variables that represent which variables are included in a model, which allows positive probability that the coefficients are exactly zero. Using the Jeffreys-Zellner’s g-prior for parameters in a model we derive closed form expressions for Bayes factors that are used in posterior probabilities.
