# A Gibbs Sampler for a Class of Random Convex Polytopes

@article{Jacob2021AGS, title={A Gibbs Sampler for a Class of Random Convex Polytopes}, author={Pierre E. Jacob and Ruobin Gong and Paul Thatcher Edlefsen and Arthur P. Dempster}, journal={Journal of the American Statistical Association}, year={2021}, volume={116}, pages={1181 - 1192} }

Abstract We present a Gibbs sampler for the Dempster–Shafer (DS) approach to statistical inference for categorical distributions. The DS framework extends the Bayesian approach, allows in particular the use of partial prior information, and yields three-valued uncertainty assessments representing probabilities “for,” “against,” and “don’t know” about formal assertions of interest. The proposed algorithm targets the distribution of a class of random convex polytopes which encapsulate the DS… Expand

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Comment on “A Gibbs Sampler for a Class of Random Convex Polytopes”

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The paper offers answers to these questions by proposing a Gibbs sampler to perform statistical inference for categorical distributions using the Dempster-Shafer approach. To be precise, let x = (xi)… Expand

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