AI Chat AI Image Generator AI Video Text to Speech

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

04/06/2021

∙

by Jonathan P Williams, et al.

∙

∙

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.

page 1

page 2

page 3

page 4

research

∙ 10/25/2019

A Gibbs sampler for a class of random convex polytopes

We present a Gibbs sampler to implement the Dempster-Shafer (DS) theory ...

0 Pierre E. Jacob, et al. ∙

research

∙ 12/16/2021

On Gibbs Sampling for Structured Bayesian Models Discussion of paper by Zanella and Roberts

This article is a discussion of Zanella and Roberts' paper: Multilevel l...

0 Xiaodong Yang, et al. ∙

research

∙ 02/21/2018

Discussion on "Sparse graphs using exchangeable random measures" by Francois Caron and Emily B. Fox

This is a discussion on "Sparse graphs using exchangeable random measure...

0 Mingyuan Zhou, et al. ∙

research

∙ 04/15/2021

Comments on: A Gibbs sampler for a class of random convex polytopes

In this comment we discuss relative strengths and weaknesses of simplex ...

0 Kentaro Hoffman, et al. ∙

research

∙ 03/26/2018

Scalable inference for crossed random effects models

We analyze the complexity of Gibbs samplers for inference in crossed ran...

0 Omiros Papaspiliopoulos, et al. ∙

research

∙ 05/01/2020

Rejoinder for the discussion of the paper "A novel algorithmic approach to Bayesian Logic Regression"

In this rejoinder we summarize the comments, questions and remarks on th...

10 Aliaksandr Hubin, et al. ∙