# Wild posteriors in the wild

arXiv: https://arxiv.org/abs/2503.00239 · HTML: https://mlsanity.com/paper/2503.00239/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2025.

## Built on by (most cited first, 2 of 2)

- [Cognitive models can reveal interpretable value trade-offs in language models](https://mlsanity.com/paper/2506.20666/) (2025)
- [Stepwise Variational Inference with Vine Copulas](https://mlsanity.com/paper/2603.22959/) (2026)

## Abstract (arXiv)

Bayesian posterior approximation has become more accessible to practitioners than ever, thanks to modern black-box software. While these tools provide highly accurate approximations with minimal user effort, certain posterior geometries remain notoriously difficult for standard methods. As a result, research into alternative approximation techniques continues to flourish. In many papers, authors validate their new approaches by testing them on posterior shapes deemed challenging or "wild." However, these shapes are not always directly linked to real-world applications where they naturally occur. In this note, we present examples of practical applications that give rise to some commonly used benchmark posterior shapes.