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Activity Number: 339
Type: Invited
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #314391 View Presentation
Title: Prior-Free Probabilistic Inference: Inferential Models
Author(s): Chuanhai Liu*
Companies: Purdue University
Keywords: Validity principle ; Efficiency principle ; Dempster-Shafer theory ; Fiducial argument ; Bayesian argument ; Neyman-Pearson theory

Developing solid foundations for scientific inference is the most fundamental but unsolved problem in statistics. We argue for two basic principles for truly prior-free probabilistic inference and introduce a new framework, which is built upon the two principles and called Inferential Models (IMs). It follows as a remark that IM bridges the gap between the Bayesian and frequentist schools of thought that has been previously considered impossible. This is joint work with Ryan Martin.

Authors who are presenting talks have a * after their name.

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