Non-Informative Priors and Default Priors
Abstract
In this lecture we will go into more details about the Normal-Gamma conjugate prior and limiting cases in linear models, including Jeffreys prior.
Readings: Christensen Chapter 2 and Chapter 6, Appendix A & B as needed C
In this lecture we will continue with the Bayesian perspective for estimation in linear models, and discuss various recommendations of Conjugate Normal-Gamma priors and the resulting posterior distributions, highlighting the advantages and disadvantages of conjugate priors. We will introduce Jeffreys’ prior and formal Bayesian posterior inference, and how this may be viewed as a limiting case of a conjugate normal-gamma prior. Finally we will discuss the role of invariance in construction of default prior distributions and the resulting inference and prediction problems.
