# Principles of Bayesian Inference using General Divergence Criteria

## TL;DR

This paper extends Bayesian inference to incorporate general divergence criteria beyond KL-divergence, enhancing robustness and flexibility in statistical modeling and decision making.

## Contribution

It develops a principled Bayesian updating framework targeting diverse divergence measures, broadening the scope of Bayesian inference beyond traditional KL-based methods.

## Key findings

- Broader divergence measures can influence inference outcomes.
- The method improves robustness to model misspecification.
- Application to high-dimensional models is discussed.

## Abstract

When it is acknowledged that all candidate parameterised statistical models are misspecified relative to the data generating process, the decision maker (DM) must currently concern themselves with inference for the parameter value minimising the KL-divergence between the model and the process (Walker, 2013). However, it has long been known that minimising the KL-divergence places a large weight on correctly capturing the tails of the sample distribution. As a result the DM is required to worry about the robustness of their model to tail misspecifications if they want to conduct principled inference. In this paper we alleviate these concerns for the DM. We advance recent methodological developments in general Bayesian updating (Bissiri, Holmes and Walker, 2016) to propose a statistically well principled Bayesian updating of beliefs targeting the minimisation of more general divergence criteria. We improve both the motivation and the statistical foundations of existing Bayesian minimum divergence estimation (Hooker and Vidyashankar, 2014; Ghosh and Basu, 2016), allowing the well principled Bayesian to target predictions from the model that are close to the genuine model in terms of some alternative divergence measure to the KL-divergence. Our principled formulation allows us to consider a broader range of divergences than have previously been considered. In fact we argue defining the divergence measure forms an important, subjective part of any statistical analysis, and aim to provide some decision theoretic rational for this selection. We illustrate how targeting alternative divergence measures can impact the conclusions of simple inference tasks, and discuss then how our methods might apply to more complicated, high dimensional models.