Bayesian predictive densities when the observed data and the target variable to be predicted have different distributions are investigated by using the framework of information geometry. The performance of predictive densities is evaluated by the Kullback–Leibler divergence. The parametric models are formulated as Riemannian manifolds. In the conventional setting in which and have the same distribution, the Fisher–Rao metric and the Jeffreys prior play essential roles. In the present setting in which and have different distributions, a new metric, which we call the predictive metric, constructed by using the Fisher information matrices of and , and the volume element based on the predictive metric play the corresponding roles. It is shown that Bayesian predictive densities based on priors constructed by using non-constant positive superharmonic functions with respect to the predictive metric asymptotically dominate those based on the volume element prior of the predictive metric.
"Asymptotic Properties of Bayesian Predictive Densities When the Distributions of Data and Target Variables are Different." Bayesian Anal. 10 (1) 31 - 51, March 2015. https://doi.org/10.1214/14-BA886