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Activity Number: 83
Type: Invited
Date/Time: Sunday, July 30, 2017 : 8:30 PM to 10:30 PM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #323642
Title: The Combination of Confirmatory and Contradictory Statistical Evidence at Low Resolution
Author(s): Ruobin Gong* and Xiao-Li Meng
Companies: Harvard University and Harvard University
Keywords: belief function ; random sets ; Dempster-Shafer ; low-resolution inference ; Bayesian inference ; information aggregation
Abstract:

The Dempster-Shafer (DS) theory of belief functions is a generalization of Bayesian inference. It mobilizes the mathematical construct of random sets to describe uncertainty. Random sets make up a language far richer than random variables or vectors. They can truthfully encode partial, low-resolution, or even the lack of knowledge, which are otherwise inexpressible ideas in statistical models. We examine random sets in most plain probabilistic terms, and discuss implications on the aggregation of statistical evidence, in particular confirmatory vs contradictory inputs in this context.


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