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Title: A Gibbs sampler for a class of random convex polytopes Authors:  Ruobin Gong - Rutgers University (United States) [presenting]
Abstract: A Gibbs sampler is presented 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 inference. The sampler relies on an equivalence between the iterative constraints of the vertex configuration and the non-negativity of cycles in a fully connected directed graph.