Papers › A Gibbs sampler for a class of random convex polytopes
A Gibbs sampler for a class of random convex polytopes
Pierre E. Jacob, Ruobin Gong, Paul T. Edlefsen, Arthur P. Dempster
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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 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. Illustrations include the testing of independence in 2x2 contingency tables and parameter estimation of the linkage model.
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