Under Review

A Gibbs sampler for a class of random convex polytopes

Computation categorical data Monte Carlo

Cite as:

Pierre Jacob and Ruobin Gong and Paul Edlefsen and Arthur Dempster (2019). A Gibbs sampler for a class of random convex polytopes. RESEARCHERS.ONE, https://www.researchers.one/article/2019-10-14.


We present a Gibbs sampler to implement the Dempster-Shafer (DS) theory of statistical inference for Categorical distributions with arbitrary numbers of categories and observations. The DS framework is trademarked by its three-valued uncertainty assessment (p, q, r), probabilities "for", "against", and "don't know", associated with formal assertions of interest. The proposed algorithm targets the invariant distribution of a class of random convex polytopes which encapsulate the inference, via establishing an equivalence between the iterative constraints of the vertex configuration and the non-negativity of cycles in a fully connected directed graph. The computational cost increases with the size of the input, linearly with the number of observations and polynomially in the number of non-empty categories. Illustrations of numerical examples include the testing of independence in 2 by 2 contingency tables and parameter estimation of the linkage model. Results are compared to alternative methods of Categorical inference.