# Calibrated Generalized Bayesian Inference

arXiv: https://arxiv.org/abs/2311.15485 · HTML: https://mlsanity.com/paper/2311.15485/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2023.

## Built on by (most cited first, 1 of 1)

- [Spatio-temporal probabilistic forecast using MMAF-guided learning](https://mlsanity.com/paper/2603.15055/) (2026)

## Abstract (arXiv)

We propose a simple approach that provides accurate uncertainty quantification for Bayesian inference in misspecified or approximate models, and for generalized (Gibbs) posteriors. While existing solutions in this context are based on explicit Gaussian approximations or post-processing procedures, we demonstrate that correct uncertainty quantification can be achieved by substituting the usual posterior with an intuitively appealing alternative that conveys the same information. This solution applies to both likelihood-based and loss-based posteriors, and is formally demonstrated to reliably quantify uncertainty. This new approach is demonstrated through a range of examples, including generalized linear models, and doubly intractable models.