On Reparameterization Invariant Bayesian Point Estimates and Credible Regions

TL;DR

This paper explores reparameterization invariant Bayesian point estimates and credible regions, highlighting how different intrinsic loss functions influence predictive performance and statistical properties of estimates.

Contribution

It introduces and analyzes intrinsic loss functions based on Kullback-Leibler divergence, linking them to predictive accuracy and unbiased estimation in Bayesian inference.

Findings

Kullback-Leibler divergence from full to restricted model aligns with predictive criteria.

Alternative divergence yields unbiased, minimum variance estimates for location and scale.

Different intrinsic loss functions impact the properties of Bayesian estimates.

Abstract

This paper considers reparameterization invariant Bayesian point estimates and credible regions of model parameters for scientific inference and communication. The effect of intrinsic loss function choice in Bayesian intrinsic estimates and regions is studied with the following findings. A particular intrinsic loss function, using Kullback-Leibler divergence from the full model to the restricted model, has strong connection to a Bayesian predictive criterion, which produces point estimates with the best predictive performance. An alternative intrinsic loss function, using Kullback-Leibler divergence from the restricted model to the full model, produces estimates with interesting frequency properties for at least some commonly used distributions, that is, unbiased minimum variance estimates of the location and scale parameters.