JACOB Pierre, GONG Ruobin, EDLEFSEN Paul T., DEMPSTER Arthur P.
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 nonnegativity of cycles in a fully connected directed graph. Illustrations include the testing of independence in 2 × 2 contingency tables and parameter estimation of the linkage model.
JACOB, P., GONG, R., EDLEFSEN, P.T. et DEMPSTER, A.P. (2021). A Gibbs Sampler for a Class of Random Convex Polytopes. Journal of the American Statistical Association, 116(535), pp. 1181-1192.