## Bayesian Analysis

### Asymptotic Properties of Bayesian Predictive Densities When the Distributions of Data and Target Variables are Different

Fumiyasu Komaki

#### Abstract

Bayesian predictive densities when the observed data $x$ and the target variable $y$ 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 $x$ and $y$ have the same distribution, the Fisher–Rao metric and the Jeffreys prior play essential roles. In the present setting in which $x$ and $y$ have different distributions, a new metric, which we call the predictive metric, constructed by using the Fisher information matrices of $x$ and $y$, 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.

#### Article information

Source
Bayesian Anal. Volume 10, Number 1 (2015), 31-51.

Dates
First available in Project Euclid: 28 January 2015

https://projecteuclid.org/euclid.ba/1422468422

Digital Object Identifier
doi:10.1214/14-BA886

Mathematical Reviews number (MathSciNet)
MR3420896

Zentralblatt MATH identifier
1335.62053

#### Citation

Komaki, Fumiyasu. Asymptotic Properties of Bayesian Predictive Densities When the Distributions of Data and Target Variables are Different. Bayesian Anal. 10 (2015), no. 1, 31--51. doi:10.1214/14-BA886. https://projecteuclid.org/euclid.ba/1422468422

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