On Bayesian Estimation via Divergences

TL;DR

This paper introduces a novel Bayesian inference methodology using phi-divergences and duality, establishing the asymptotic properties of the estimates, offering a new approach to Bayesian analysis.

Contribution

It proposes a new divergence-based Bayesian inference method utilizing duality, with proven asymptotic behavior, advancing the theoretical framework of Bayesian estimation.

Findings

Asymptotic laws of the divergence-based estimates are established.

The methodology provides a new perspective on Bayesian inference.

The approach extends existing divergence methods to Bayesian settings.

Abstract

In this Note we introduce a new methodology for Bayesian inference through the use of $\phi$-divergences and the duality technique. The asymptotic laws of the estimates are established.