The Effect of Improper Priors on Gibbs Sampling in Hierarchical Linear Mixed Models

Abstract

Abstract Often, either from a lack of prior information or simply for convenience, variance components are modeled with improper priors in hierarchical linear mixed models. Although the posterior distributions for these models are rarely available in closed form, the usual conjugate structure of the prior specification allows for painless calculation of the Gibbs conditionals. Thus the Gibbs sampler may be used to explore the posterior distribution without ever having established propriety of the posterior. An example is given showing that the output from a Gibbs chain corresponding to an improper posterior may appear perfectly reasonable. Thus one cannot expect the Gibbs output to provide a “red flag,” informing the user that the posterior is improper. The user must demonstrate propriety before a Markov chain Monte Carlo technique is used. A theorem is given that classifies improper priors according to the propriety of the resulting posteriors. Applications concerning Bayesian analysis of animal breeding data and the location of maxima of unwieldy (restricted) likelihood functions are discussed. Gibbs sampling with improper posteriors is then considered in more generality. The concept of functional compatibility of conditional densities is introduced and is used to construct an invariant measure for a class of Markov chains. These results are used to show that Gibbs chains corresponding to improper posteriors are, in theory, quite ill-behaved.