# Discussion of "A Gibbs sampler for a class of random convex polytopes"

arXiv: https://arxiv.org/abs/2104.02717 · HTML: https://mlsanity.com/paper/2104.02717/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2021.

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

- [Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)](https://mlsanity.com/paper/2211.16557/) (2022)
- [Model-free generalized fiducial inference](https://mlsanity.com/paper/2307.12472/) (2023)

## Abstract (arXiv)

An exciting new algorithmic breakthrough has been advanced for how to carry out inferences in a Dempster-Shafer (DS) formulation of a categorical data generating model. The developed sampling mechanism, which draws on theory for directed graphs, is a clever and remarkable achievement, as this has been an open problem for many decades. In this discussion, I comment on important contributions, central questions, and prevailing matters of the article.