MCMC with mixtures of g-priors and properties of estimators

Overview

In this lecture we will illustrate MCMC sampling with the Cauchy prior as mixtures of g-priors and look at properties of estimators.

Readings
Links
Reading Slides Code

Readings: Christensen Chapter 2 and Hoff Chapter 9

In this lecture we will illustrate MCMC for estimation with Cauchy priors, represented as a scale mixture of normals using the program JAGS with R. We will exame theoretical properties of the Bayes estimators in terms of Mean Squared Error using squared error loss.